diff --git a/docs/docs/tutorials/fitting-bayesian.ipynb b/docs/docs/tutorials/fitting-bayesian.ipynb
index 77e8b884..1ee6da97 100644
--- a/docs/docs/tutorials/fitting-bayesian.ipynb
+++ b/docs/docs/tutorials/fitting-bayesian.ipynb
@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib widget"
+    "%matplotlib inline"
    ]
   },
   {
@@ -28,7 +28,7 @@
     "\n",
     "where $\\theta$ are the model parameters, $d$ is the observed data, $p(d \\mid \\theta)$ is the likelihood, and $p(\\theta)$ is the prior. In `easyscience`, the `min`/`max` bounds of a `Parameter` are interpreted as a **uniform prior**, and a Gaussian likelihood is constructed from the data and supplied weights.\n",
     "\n",
-    "`easyscience` exposes a Bayesian Markov-chain Monte Carlo (MCMC) sampler through the `Fitter.mcmc_sample` method.  Under the hood this uses BUMPS' DREAM sampler, so the underlying minimizer must be switched to BUMPS.\n",
+    "`easyscience` exposes a Bayesian Markov-chain Monte Carlo (MCMC) sampler through the `Sampler` class.  Under the hood this uses BUMPS' DREAM sampler, so the underlying minimizer must be switched to BUMPS.\n",
     "\n",
     "```{note}\n",
     "This tutorial focuses on Bayesian analysis with a simple QENS model for illustration. For dedicated QENS fitting with more sophisticated models, consider using [`EasyDynamics`](https://github.com/easyscience/easydynamics).\n",
@@ -45,7 +45,7 @@
     "\n",
     "The trade-off is computational cost: MCMC requires thousands of model evaluations, whereas an MLE fit may converge in dozens. For the simple 4-parameter model used here the difference is negligible, but for expensive models it is worth starting with MLE and only switching to MCMC when you need the richer output.\n",
     "\n",
-    "In this tutorial we re-use the QENS dataset and the Lorentzian-with-resolution model from the [Fitting QENS](fitting-qens.ipynb) tutorial, but instead of returning a single best-fit value with a symmetric error bar we will draw thousands of samples from the posterior.\n"
+    "In this tutorial we re-use the QENS dataset and the Lorentzian-with-resolution model from the [Fitting QENS](fitting-qens.ipynb) tutorial, but instead of returning a single best-fit value with a symmetric error bar we will draw thousands of samples from the posterior."
    ]
   },
   {
@@ -250,20 +250,20 @@
     "\n",
     "We now draw samples from the posterior distribution $p(\\theta \\mid d)$ using the BUMPS DREAM (DiffeRential Evolution Adaptive Metropolis) algorithm. DREAM is an ensemble MCMC method that runs multiple chains in parallel and automatically tunes the proposal distribution.\n",
     "\n",
-    "DREAM only works with the BUMPS minimizer. We reuse the ``mle_fitter`` created above, switch it to BUMPS, and call `Fitter.mcmc_sample`, which returns a dictionary with the following keys:\n",
+    "DREAM only works with the BUMPS minimizer. We reuse the ``mle_fitter`` created above, switch it to BUMPS, and create a `Sampler` instance bound to the fitter and data. Calling `sampler.sample()` returns a `SamplingResults` object with the following attributes:\n",
     "\n",
     "- `draws`: a `(n_samples, n_parameters)` array of posterior samples: each **row** is one complete draw from the joint posterior (one value for every parameter simultaneously), and each **column** holds all sampled values for a single parameter;\n",
     "- `param_names`: the unique names of the parameters, in the same column order as `draws`;\n",
-    "- `internal_bumps_object`: the underlying BUMPS `MCMCDraw` object, useful for advanced diagnostics;\n",
+    "- `state`: the underlying BUMPS `MCMCDraw` object, useful for advanced diagnostics;\n",
     "- `logp`: the log-posterior of each retained sample.\n",
     "\n",
     "The key sampling parameters are:\n",
     "\n",
-    "- `samples` (4000): the total number of posterior draws to generate across all chains;\n",
+    "- `samples` (10000): the total number of posterior draws to generate across all chains;\n",
     "- `burn` (500): the number of initial *burn-in* iterations to discard — the sampler needs time to find the typical set of the posterior, and early samples are not representative;\n",
     "- `thin` (2): the *thinning* interval — only every second sample is kept, which reduces autocorrelation between consecutive draws;\n",
     "\n",
-    "First, we switch to the BUMPS minimizer:\n"
+    "First, we switch to the BUMPS minimizer:"
    ]
   },
   {
@@ -285,17 +285,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = mle_fitter.mcmc_sample(\n",
-    "    x=omega,\n",
-    "    y=intensity_obs,\n",
-    "    weights=1 / intensity_error,\n",
-    "    samples=10000,\n",
-    "    burn=500,\n",
-    "    thin=2,\n",
-    ")\n",
+    "from easyscience.fitting import Sampler\n",
+    "\n",
+    "sampler = Sampler(mle_fitter, omega, intensity_obs, weights=1 / intensity_error)\n",
+    "results = sampler.sample(samples=10000, burn=500, thin=2)\n",
     "\n",
-    "print(f'Drew {result[\"draws\"].shape[0]} samples for {result[\"draws\"].shape[1]} parameters.')\n",
-    "print('parameters:', result['param_names'])"
+    "print(f'Drew {results.draws.shape[0]} samples for {results.draws.shape[1]} parameters.')\n",
+    "print('parameters:', results.param_names)"
    ]
   },
   {
@@ -320,12 +316,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "draws = result['draws']\n",
-    "logp = result['logp']\n",
-    "if callable(logp):\n",
-    "    _, logp = logp()\n",
-    "    logp = logp.flatten()\n",
-    "name_to_col = {name: idx for idx, name in enumerate(result['param_names'])}\n",
+    "draws = results.draws\n",
+    "logp = results.logp\n",
+    "name_to_col = {name: idx for idx, name in enumerate(results.param_names)}\n",
     "\n",
     "\n",
     "def column_for(parameter):\n",
@@ -367,7 +360,7 @@
     "The MLE finds the single point that maximises the likelihood, while the Bayesian median is the central value of the *posterior* — which also accounts for the prior $p(\\theta)$. If a parameter's posterior is skewed (asymmetric), the median and the mode (which approximates the MLE) will not coincide. The table below shows both so you can spot any such differences.\n",
     "\n",
     "\n",
-    "Note that the columns of `result['draws']` are ordered by `result['param_names']` (which use the parameters' `unique_name`), so it is worth building a small helper to look up a column by friendly name.\n"
+    "Note that the columns of `results.draws` are ordered by `results.param_names` (which use the parameters' `unique_name`), so it is worth building a small helper to look up a column by friendly name."
    ]
   },
   {
@@ -492,11 +485,125 @@
     "ax.legend()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03339658",
+   "metadata": {},
+   "source": [
+    "## Extend the chain and check convergence\n",
+    "\n",
+    "The original run used ``samples=10000`` (5000 retained after thinning).  Here we **extend the chain by 5000 more raw samples** using ``sampler.extend()`` — DREAM continues from the previous in-memory state instead of starting cold.\n",
+    "\n",
+    "``sampler.extend(additional_samples=5000)`` does the ring-buffer arithmetic for you automatically: it reads the number of stored generations from the saved state and sizes the new buffer to ``old_generations + additional_samples`` so no existing draws are lost.\n",
+    "\n",
+    "After the extension we compare the posterior summaries and check convergence with Gelman-Rubin R-hat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "293b140b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extend the existing chain by 5000 more raw samples.\n",
+    "# extend() handles the ring-buffer arithmetic automatically.\n",
+    "extended_results = sampler.extend(additional_samples=5000, thin=2)\n",
+    "\n",
+    "short_draws = results.draws\n",
+    "extended_draws = extended_results.draws\n",
+    "\n",
+    "print(f'Short chain: {short_draws.shape[0]} retained draws')\n",
+    "print(f'Extended chain: {extended_draws.shape[0]} retained draws')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ec4302c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build helpers to look up parameter columns.\n",
+    "name_to_col_ext = {name: idx for idx, name in enumerate(extended_results.param_names)}\n",
+    "name_to_col_orig = {name: idx for idx, name in enumerate(results.param_names)}\n",
+    "\n",
+    "\n",
+    "def col_orig(par):\n",
+    "    return results.draws[:, name_to_col_orig[par.unique_name]]\n",
+    "\n",
+    "\n",
+    "def col_ext(par):\n",
+    "    return extended_results.draws[:, name_to_col_ext[par.unique_name]]\n",
+    "\n",
+    "\n",
+    "# Side-by-side posterior summary: compare the first 5000 draws (before extension)\n",
+    "# with the FULL extended set.\n",
+    "print(f'{\"param\":<10s} {\"metric\":>12s} {\"first 5k\":>12s} {\"full ext\":>12s}  diff')\n",
+    "print('-' * 65)\n",
+    "for label, par in (('area', area), ('gamma', gamma), ('omega_0', omega_0), ('sigma', sigma)):\n",
+    "    c_first = col_orig(par)  # first 5000 draws\n",
+    "    c_full = col_ext(par)  # full extended ~7500 draws\n",
+    "    for metric, fn in [\n",
+    "        ('mean', np.mean),\n",
+    "        ('std', np.std),\n",
+    "        ('q2.5%', lambda c: np.percentile(c, 2.5)),\n",
+    "        ('q50%', lambda c: np.percentile(c, 50)),\n",
+    "        ('q97.5%', lambda c: np.percentile(c, 97.5)),\n",
+    "    ]:\n",
+    "        vf = fn(c_first)\n",
+    "        vx = fn(c_full)\n",
+    "        diff = vx - vf\n",
+    "        print(f'{label:<10s} {metric:>12s} {vf:12.4g} {vx:12.4g} {diff:+.2e}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0f30be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visual comparison: overlay the first 5000 draws with the extended set.\n",
+    "fig, axes = plt.subplots(1, 4, figsize=(14, 3))\n",
+    "for ax, label, par in zip(\n",
+    "    axes, ('area', 'gamma', 'omega_0', 'sigma'), (area, gamma, omega_0, sigma)\n",
+    "):\n",
+    "    c_first = col_orig(par)\n",
+    "    c_full = col_ext(par)\n",
+    "    ax.hist(c_first, bins=40, density=True, alpha=0.5, color='C0', label=f'first 5k')\n",
+    "    ax.hist(\n",
+    "        c_full, bins=40, density=True, alpha=0.5, color='C3', label=f'extended ({len(c_full)})'\n",
+    "    )\n",
+    "    ax.set_title(label)\n",
+    "    ax.set_yticks([])\n",
+    "axes[0].legend(fontsize=9)\n",
+    "fig.suptitle('Marginal posterior: first 5000 draws vs extended chain')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3449e0a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convergence diagnostic: Gelman-Rubin R-hat from the extended chain state.\n",
+    "# Values close to 1.0 indicate good convergence.\n",
+    "print('Gelman-Rubin R-hat (extended chain) — values < 1.05 indicate convergence:')\n",
+    "rhat = extended_results.state.gelman()\n",
+    "for name, val in zip(extended_results.param_names, rhat):\n",
+    "    status = '✓' if val < 1.05 else '?' if val < 1.1 else '✗'\n",
+    "    print(f'  {name:<20s} {val:.4f}  {status}')"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "p312",
+   "display_name": "era",
    "language": "python",
    "name": "python3"
   },
diff --git a/pixi.lock b/pixi.lock
index 144dca8c..9d493986 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,8 +1,4 @@
-version: 7
-platforms:
-- name: linux-64
-- name: osx-arm64
-- name: win-64
+version: 6
 environments:
   default:
     channels:
@@ -10,17 +6,57 @@ environments:
     - url: https://conda.anaconda.org/conda-forge/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.5-py314hd8ed1ab_100.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.21-py314h42812f9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyha191276_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.14.0-pyh53cf698_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-ha1258a1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20260107.1-cxx17_h7b12aa8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
@@ -28,7 +64,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.0-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda
@@ -36,51 +72,246 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.21-h280c20c_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.22-h280c20c_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.2-h0c1763c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42-h5347b49_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.1-h5347b49_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.52.1-h280c20c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py314h67df5f8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/msgspec-0.21.1-py314h5bd0f2a_0.conda
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+  - idna>=2.0
+  - multidict>=4.0
+  - propcache>=0.2.1
   requires_python: '>=3.10'
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+  - idna>=2.0
+  - multidict>=4.0
+  - propcache>=0.2.1
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl
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-  - pytest-xdist ; extra == 'test-no-images'
-  - wurlitzer ; extra == 'test-no-images'
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-  requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h09e67af_11.conda
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+  license: MPL-2.0
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+  purls: []
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h10816f8_11.conda
+  sha256: 01fd50d2801b23b59fafea6bf704a6c5faf0f5969104400eae0e6572cb2e5304
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+  - __osx >=11.0
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+  md5: f016c0c5f9c01549b259146614786192
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+  - libsodium >=1.0.22,<1.0.23.0a0
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+  md5: ba3dcdc8584155c97c648ae9c044b7a3
+  depends:
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+  - python
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+  license_family: MIT
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+  license_family: BSD
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+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 433413
+  timestamp: 1764777166076
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
+  sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2
+  md5: 053b84beec00b71ea8ff7a4f84b55207
+  depends:
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+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 388453
+  timestamp: 1764777142545
diff --git a/pyproject.toml b/pyproject.toml
index e555f419..014b42ca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -211,7 +211,7 @@ select = [
 # Ignore specific rules globally
 ignore = [
   'COM812', # https://docs.astral.sh/ruff/rules/missing-trailing-comma/
-  # The following is replaced by 'D'/[tool.ruff.lint.pydocstyle] and [tool.pydoclint]
+  # Replaced by 'D' plus pydocstyle and pydoclint
   'DOC', # https://docs.astral.sh/ruff/rules/#pydoclint-doc
   # Disable, as [tool.format_docstring] split one-line docstrings into the canonical multi-line layout
   'D200', # https://docs.astral.sh/ruff/rules/unnecessary-multiline-docstring/
diff --git a/src/easyscience/fitting/__init__.py b/src/easyscience/fitting/__init__.py
index 54bad74a..cb9e691f 100644
--- a/src/easyscience/fitting/__init__.py
+++ b/src/easyscience/fitting/__init__.py
@@ -4,8 +4,10 @@
 from .available_minimizers import AvailableMinimizers
 from .fitter import Fitter
 from .minimizers.utils import FitResults
+from .sampler import Sampler
+from .sampler import SamplingResults
 
 # Causes circular import
 # from .multi_fitter import MultiFitter  # noqa: F401, E402
 
-all = [AvailableMinimizers, Fitter, FitResults]
+all = [AvailableMinimizers, Fitter, FitResults, Sampler, SamplingResults]
diff --git a/src/easyscience/fitting/fitter.py b/src/easyscience/fitting/fitter.py
index 38f91ddb..70dcfed0 100644
--- a/src/easyscience/fitting/fitter.py
+++ b/src/easyscience/fitting/fitter.py
@@ -418,105 +418,3 @@ def _post_compute_reshaping(
         fit_result.y_calc = np.reshape(fit_result.y_calc, y.shape)
         fit_result.y_err = np.reshape(fit_result.y_err, y.shape)
         return fit_result
-
-    def mcmc_sample(
-        self,
-        x: np.ndarray,
-        y: np.ndarray,
-        weights: np.ndarray,
-        samples: int = 10000,
-        burn: int = 2000,
-        thin: int = 10,
-        population: Optional[int] = None,
-        vectorized: bool = False,
-        sampler_kwargs: Optional[dict] = None,
-        progress_callback: Optional[Callable[[dict], Optional[bool]]] = None,
-        abort_test: Optional[Callable[[], bool]] = None,
-    ) -> dict:
-        """Run Bayesian MCMC sampling using the BUMPS DREAM sampler.
-
-        Works with both a plain ``Fitter`` (single dataset) and a
-        ``MultiFitter`` (multiple datasets) via polymorphic dispatch:
-        ``_precompute_reshaping`` and ``_fit_function_wrapper`` are resolved
-        on the concrete subclass at call time, so multi-dataset flattening
-        is handled automatically when called on a ``MultiFitter`` instance.
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Independent variable array (or list of arrays for ``MultiFitter``).
-        y : np.ndarray
-            Dependent variable array (or list of arrays for ``MultiFitter``).
-        weights : np.ndarray
-            Weight array (or list of arrays for ``MultiFitter``).
-        samples : int, default=10000
-            Number of retained DREAM samples requested from BUMPS.
-        burn : int, default=2000
-            Burn-in steps to discard before collecting samples.
-        thin : int, default=10
-            Thinning interval — only every ``thin``-th sample is kept,
-            which reduces autocorrelation between consecutive draws.
-        population : Optional[int], default=None
-            BUMPS DREAM population count (number of parallel chains).
-        vectorized : bool, default=False
-            When ``True``, each x array may be multi-dimensional (e.g. an
-            ``(N, M, 2)`` grid for a 2D model) and is left as-is.  When
-            ``False`` (default), each x array is expected to be 1-D.
-        sampler_kwargs : Optional[dict], default=None
-            Additional keyword arguments forwarded to the BUMPS DREAM sampler.
-        progress_callback : Optional[Callable[[dict], Optional[bool]]], default=None
-            Optional callback invoked at each DREAM generation.  The payload
-            dict includes ``iteration`` and ``sampling: True``.
-        abort_test : Optional[Callable[[], bool]], default=None
-            Optional callable that returns ``True`` to abort sampling early.
-
-        Returns
-        -------
-        dict
-            Dictionary with keys ``'draws'``, ``'param_names'``,
-            ``'internal_bumps_object'``, and ``'logp'``.
-
-        Raises
-        ------
-        ValueError
-            If ``samples``, ``burn``, or ``thin`` are invalid.
-        RuntimeError
-            If the active minimizer is not a BUMPS instance.
-        """
-        if not isinstance(samples, int) or samples <= 0:
-            raise ValueError('samples must be a positive integer.')
-        if not isinstance(burn, int) or burn < 0:
-            raise ValueError('burn must be a non-negative integer.')
-        if not isinstance(thin, int) or thin < 1:
-            raise ValueError('thin must be a positive integer.')
-
-        x_fit, x_new, y_new, w_new, dims = self._precompute_reshaping(x, y, weights, vectorized)
-        self._dependent_dims = dims
-
-        original_fit_func = self._fit_function
-        self.fit_function = self._fit_function_wrapper(x_new, flatten=True)
-
-        try:
-            minimizer = self.minimizer
-            if not (hasattr(minimizer, 'package') and minimizer.package == 'bumps'):
-                raise RuntimeError(
-                    'Bayesian sampling requires a BUMPS minimizer. '
-                    'Use ``fitter.switch_minimizer(AvailableMinimizers.Bumps)`` first.'
-                )
-
-            result = minimizer.mcmc_sample(
-                x=x_fit,
-                y=y_new,
-                weights=w_new,
-                samples=samples,
-                burn=burn,
-                thin=thin,
-                population=population,
-                sampler_kwargs=sampler_kwargs,
-                progress_callback=progress_callback,
-                abort_test=abort_test,
-            )
-        finally:
-            self.fit_function = original_fit_func
-
-        return result
diff --git a/src/easyscience/fitting/minimizers/__init__.py b/src/easyscience/fitting/minimizers/__init__.py
index ca70e748..478ff388 100644
--- a/src/easyscience/fitting/minimizers/__init__.py
+++ b/src/easyscience/fitting/minimizers/__init__.py
@@ -3,9 +3,20 @@
 
 from .minimizer_base import MinimizerBase
 from .minimizer_bumps import Bumps
+from .minimizer_bumps import load_sampler_state
+from .minimizer_bumps import save_sampler_state
 from .minimizer_dfo import DFO
 from .minimizer_lmfit import LMFit
 from .utils import FitError
 from .utils import FitResults
 
-__all__ = [MinimizerBase, Bumps, DFO, LMFit, FitError, FitResults]
+__all__ = [
+    MinimizerBase,
+    Bumps,
+    DFO,
+    LMFit,
+    FitError,
+    FitResults,
+    save_sampler_state,
+    load_sampler_state,
+]
diff --git a/src/easyscience/fitting/minimizers/minimizer_bumps.py b/src/easyscience/fitting/minimizers/minimizer_bumps.py
index 40b303f9..198472ce 100644
--- a/src/easyscience/fitting/minimizers/minimizer_bumps.py
+++ b/src/easyscience/fitting/minimizers/minimizer_bumps.py
@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import copy
+import math
 from typing import Any
 from typing import Callable
 
@@ -389,6 +390,7 @@ def mcmc_sample(
         burn: int = 2000,
         thin: int = 10,
         population: int | None = None,
+        resume_state: Any | None = None,
         sampler_kwargs: dict | None = None,
         progress_callback: Callable[[dict], bool | None] | None = None,
         abort_test: Callable[[], bool] | None = None,
@@ -397,7 +399,7 @@ def mcmc_sample(
 
         Builds a BUMPS `FitProblem` from the current model and runs the DREAM
         sampler.  This is the public minimizer-level entry point for Bayesian
-        sampling; the higher-level `MultiFitter.mcmc_sample` delegates to this
+        sampling; the higher-level `Sampler` delegates to this
         method after flattening multi-dataset arrays.
 
         Parameters
@@ -415,7 +417,30 @@ def mcmc_sample(
         thin : int, default=10
             Thinning interval.
         population : int | None, default=None
-            BUMPS DREAM population count (number of parallel chains).
+            BUMPS DREAM population count per parameter (number of parallel
+            chains): BUMPS creates ``ceil(population * n_parameters)`` chains.
+        resume_state : Any | None, default=None
+            A BUMPS ``MCMCDraw`` state object from a previous
+            ``mcmc_sample()`` call (e.g. ``PosteriorResults.sampler_state``).
+            When provided, DREAM **continues** the saved chain instead of
+            starting cold.  The population, parameter count, and parameter
+            names must match the current model — a ``ValueError`` is raised
+            otherwise.
+
+            ``samples`` must be the **total** number of retained draws, not
+            an increment: to extend an existing chain of ``N`` draws by ``M``,
+            pass ``samples=N + M`` (DREAM keeps only the last ``samples``
+            draws). The :meth:`Sampler.extend` helper computes this for you.
+
+            ``burn`` is forced to 0 on resume: a previously-converged chain is
+            never re-burned.
+
+            The ``population`` and ``initializer`` parameters
+            have **no effect** when ``resume_state`` is provided — they
+            are determined by the saved state.
+
+            Resuming against *different* data is undefined behaviour (the
+            chain's likelihood changes underneath it).
         sampler_kwargs : dict | None, default=None
             Additional keyword arguments forwarded to `bumps.fitters.fit`.
         progress_callback : Callable[[dict], bool | None] | None, default=None
@@ -436,8 +461,10 @@ def mcmc_sample(
         Raises
         ------
         ValueError
-            If the input shapes or weights are invalid, or if
-            ``progress_callback`` is not callable.
+            If the input shapes or weights are invalid, if
+            ``progress_callback`` is not callable, or if ``resume_state``
+            is incompatible with the current model (parameter count,
+            names/order, or population mismatch).
         FitError
             If DREAM sampling was aborted by the user (via ``abort_test``).
         Exception
@@ -478,10 +505,100 @@ def mcmc_sample(
         curve = model_func(x, y, weights)
         problem = FitProblem(curve)
 
-        # Build DREAM kwargs
+        pop = population
+
+        # --- Resume-state compatibility validation ---
+        if resume_state is not None:
+            # Parameter count
+            n_fresh_params = len(problem._parameters)
+            state_nvar = resume_state.Nvar
+            if n_fresh_params != state_nvar:
+                raise ValueError(
+                    f'resume_state has {state_nvar} parameters but the current '
+                    f'model has {n_fresh_params}. The model must have the same '
+                    f'number of fitted parameters as when the saved chain was created.'
+                )
+
+            # Parameter names / order.
+            #
+            # BUMPS ``save_state``/``load_state`` does **not** preserve the
+            # original parameter labels: a reloaded ``MCMCDraw`` comes back
+            # with default labels like ``['P0', 'P1', ...]``. We can therefore
+            # only validate names when the state still carries our prefixed
+            # labels, i.e. an in-memory state from the current session. For a
+            # reloaded state we fall back to the parameter-count check (done
+            # above) plus an order-based warning, because BUMPS resumes
+            # positionally by column order, not by name.
+            fresh_names = [p.name[len(MINIMIZER_PARAMETER_PREFIX) :] for p in problem._parameters]
+            state_labels = list(resume_state.labels)
+            state_prefix = MINIMIZER_PARAMETER_PREFIX
+            labels_carry_our_names = bool(state_labels) and all(
+                lbl.startswith(state_prefix) for lbl in state_labels
+            )
+            if labels_carry_our_names:
+                state_stripped = [lbl[len(state_prefix) :] for lbl in state_labels]
+                if fresh_names != state_stripped:
+                    raise ValueError(
+                        f'Parameter names/order mismatch between the current model '
+                        f'and resume_state.\n'
+                        f'  Current model : {fresh_names}\n'
+                        f'  resume_state  : {state_stripped}'
+                    )
+            else:
+                from easyscience import global_object
+
+                global_object.log.getLogger('fitting.bumps').warning(
+                    'resume_state does not carry parameter names (it was most '
+                    'likely reloaded from disk, where BUMPS does not preserve '
+                    'labels). Parameter-name validation is skipped; the saved '
+                    'chain is matched to the current model by parameter order. '
+                    'Ensure this is the same model, with parameters in the same '
+                    'order, used to create the chain.'
+                )
+
+            # Population: BUMPS interprets ``pop`` as a **scale factor**.
+            # ``initpop.generate(problem, pop=P)`` creates
+            # ``ceil(P * n_params)`` chains, which becomes ``state.Npop``.
+            # On resume we must recover the scale factor from the state.
+            n_params = len(problem._parameters)
+            if pop is not None:
+                # Caller explicitly set population — validate it
+                # produces the same Npop as the saved state.
+                expected_npop = int(math.ceil(pop * n_params))
+                if expected_npop != resume_state.Npop:
+                    raise ValueError(
+                        f'Requested population ({pop}) would produce '
+                        f'{expected_npop} chains but the saved state has '
+                        f'{resume_state.Npop} chains. The population cannot '
+                        f'be changed on resume.'
+                    )
+            else:
+                # Recover the original scale factor from the state's Npop.
+                # Try ceil-division first, then fall back to floor.
+                recovered = int(math.ceil(resume_state.Npop / n_params))
+                if int(math.ceil(recovered * n_params)) != resume_state.Npop:
+                    recovered = int(resume_state.Npop / n_params)
+                pop = recovered
+
+            # Force burn=0 on resume: re-burning a previously converged
+            # chain discards good draws and is almost always a mistake.
+            if burn > 0:
+                from easyscience import global_object
+
+                global_object.log.getLogger('fitting.bumps').warning(
+                    f'burn={burn} ignored on resume: a previously converged '
+                    f'chain is not re-burned. Forcing burn=0.'
+                )
+            burn = 0
+
+        # Build DREAM kwargs. Use the resolved ``pop`` (the alias result, or
+        # the value recovered from the saved state on resume), not the raw
+        # ``population`` argument — on resume ``population`` is ``None`` while
+        # ``pop`` holds the scale factor that reproduces the saved state's
+        # population, which BUMPS requires to match.
         dream_kwargs: dict = {'samples': samples, 'burn': burn, 'thin': thin}
-        if population is not None:
-            dream_kwargs['pop'] = population
+        if pop is not None:
+            dream_kwargs['pop'] = pop
         if sampler_kwargs:
             dream_kwargs.update(sampler_kwargs)
 
@@ -493,7 +610,7 @@ def mcmc_sample(
             # Compute total DREAM steps for progress display (burn + sampling generations).
             # BUMPS DREAM default population count is 10 when not specified by the user.
             _dream_default_pop = 10
-            pop_val = population if population is not None else _dream_default_pop
+            pop_val = pop if pop is not None else _dream_default_pop
             _total_steps = burn + (samples + pop_val - 1) // pop_val
             monitors.append(
                 BumpsProgressMonitor(
@@ -521,7 +638,16 @@ def mcmc_sample(
         global_object.stack.enabled = False
 
         try:
-            x_opt, fx = driver.fit()
+            fit_kwargs = {}
+            if resume_state is not None:
+                # Defensive copy: BUMPS mutates the state object in-place
+                # (via MCMCDraw.resize() — see bumps/dream/core.py allocate_state)
+                # during resume.  Without a copy, the caller's original state
+                # object is silently altered, making it impossible to compare
+                # pre- and post-resume state (shape mismatch).  See
+                # https://github.com/easyscience/core/pull/257
+                fit_kwargs['fit_state'] = copy.deepcopy(resume_state)
+            x_opt, fx = driver.fit(**fit_kwargs)
             result_state = getattr(driver.fitter, 'state', None)
             if result_state is None:
                 raise FitError('Sampling aborted by user')
@@ -531,9 +657,10 @@ def mcmc_sample(
         finally:
             global_object.stack.enabled = stack_status
 
-        draws = result_state.draw().points
+        _draw = result_state.draw()
+        draws = _draw.points
         param_names = [p.name[len(MINIMIZER_PARAMETER_PREFIX) :] for p in problem._parameters]
-        logp = getattr(result_state, 'logp', None)
+        logp = _draw.logp
 
         return {
             'draws': draws,
@@ -686,3 +813,82 @@ def _gen_fit_results(
         results.fit_args = None
         results.engine_result = fit_results
         return results
+
+
+def save_sampler_state(state: Any, filename: str) -> None:
+    """Persist a DREAM sampler state to disk.
+
+    .. deprecated::
+        Not public API anymore — use ``Sampler.save()`` (which also writes a
+        metadata sidecar). Kept as the internal helper used by
+        ``easyscience.fitting.sampler``.
+
+    Thin wrapper around BUMPS ``save_state``. ``state`` is the
+    ``internal_bumps_object`` returned by :meth:`Bumps.mcmc_sample` (or by
+    ``Sampler``). Three gzipped text files are written:
+    ``<filename>-chain.mc.gz``, ``<filename>-point.mc.gz`` and
+    ``<filename>-stats.mc.gz``.
+
+    Parameters
+    ----------
+    state : Any
+        The BUMPS ``MCMCDraw`` object to persist.
+    filename : str
+        Path prefix for the saved files.
+    """
+    from bumps.dream.state import save_state
+
+    save_state(state, str(filename))
+
+
+def load_sampler_state(filename: str, skip: int = 0) -> Any:
+    """Load a DREAM sampler state saved by :func:`save_sampler_state`.
+
+    .. deprecated::
+        Not public API anymore — use ``Sampler.load_state()`` (which also
+        restores parameter names from the metadata sidecar). Kept as the
+        internal helper used by ``easyscience.fitting.sampler``.
+
+    The returned object can be passed as ``resume_state`` to
+    :meth:`Bumps.mcmc_sample` (or the ``Sampler``) to extend a
+    previously saved chain.
+
+    Works around a regression introduced in BUMPS 1.0.4: ``load_state`` reads
+    the saved buffers with ``numpy.loadtxt``, which collapses a single-row
+    file to a 1-D array. A short chain stores a single CR-weight update row,
+    so the subsequent ``stats[:, 0]`` indexing in ``load_state`` raises
+    ``IndexError: too many indices for array: array is 1-dimensional, but 2
+    were indexed``. We coerce each buffer read back to 2-D before
+    ``load_state`` consumes it. Older BUMPS releases (< 1.0.4) used a custom
+    2-D-safe parser and are loaded unchanged.
+
+    Parameters
+    ----------
+    filename : str
+        Path prefix that was passed to :func:`save_sampler_state`.
+    skip : int, default=0
+        Discard the first ``skip`` saved generations on load, forwarded to
+        ``bumps.dream.state.load_state``.
+
+    Returns
+    -------
+    Any
+        The reloaded BUMPS ``MCMCDraw`` object.
+    """
+    from bumps.dream import state as _bumps_state
+
+    loader = getattr(_bumps_state, 'loadtxt_with_fallback', None)
+    if loader is None:
+        # BUMPS < 1.0.4: load_state uses a custom parser that is already 2-D safe.
+        return _bumps_state.load_state(str(filename), skip=skip)
+
+    def _loadtxt_2d(file: Any, report: int = 0) -> np.ndarray:
+        arr = loader(file, report=report)
+        # A single saved row is read back as 1-D; restore it to a (1, N) row.
+        return np.atleast_2d(arr) if getattr(arr, 'ndim', 2) == 1 else arr
+
+    _bumps_state.loadtxt_with_fallback = _loadtxt_2d
+    try:
+        return _bumps_state.load_state(str(filename), skip=skip)
+    finally:
+        _bumps_state.loadtxt_with_fallback = loader
diff --git a/src/easyscience/fitting/multi_fitter.py b/src/easyscience/fitting/multi_fitter.py
index 92af9d9e..9288c703 100644
--- a/src/easyscience/fitting/multi_fitter.py
+++ b/src/easyscience/fitting/multi_fitter.py
@@ -1,8 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
-from __future__ import annotations
-
 from typing import Callable
 
 import numpy as np
diff --git a/src/easyscience/fitting/sampler.py b/src/easyscience/fitting/sampler.py
new file mode 100644
index 00000000..828e90e6
--- /dev/null
+++ b/src/easyscience/fitting/sampler.py
@@ -0,0 +1,541 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian MCMC sampling — the ``Sampler`` class and persistence helpers."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import warnings
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+from typing import Any
+from typing import Callable
+from typing import List
+from typing import Optional
+from typing import Union
+
+import numpy as np
+
+from .minimizers.minimizer_base import MINIMIZER_PARAMETER_PREFIX
+
+if TYPE_CHECKING:  # avoid import cycles; Fitter is only needed for type hints
+    from .fitter import Fitter
+
+# Sidecar schema owned by easyscience from version 2 onwards. Version 1
+# sidecars (written by easyreflectometry's save_posterior) carry only
+# 'param_names' and are still accepted on load.
+_SIDECAR_SCHEMA_VERSION = 2
+_ACCEPTED_SIDECAR_SCHEMA_VERSIONS = (1, 2)
+
+
+def _easyscience_version() -> str:
+    """Return the installed easyscience version string."""
+    try:
+        from importlib.metadata import version as _v
+
+        return _v('easyscience')
+    except Exception:
+        return 'unknown'
+
+
+def _data_fingerprint(
+    x_list: list,
+    y_list: list,
+    w_list: list,
+) -> Optional[str]:
+    """Return a SHA-256 hex digest of concatenated (x|y|weights), or None."""
+    try:
+        h = hashlib.sha256()
+        for arr in list(x_list) + list(y_list) + list(w_list):
+            h.update(np.ascontiguousarray(arr, dtype=np.float64).tobytes())
+        return h.hexdigest()
+    except Exception:
+        return None
+
+
+def save_chain(
+    state: Any,
+    param_names: Optional[List[str]],
+    path: str,
+    data_fingerprint: Optional[str] = None,
+) -> None:
+    """Persist a DREAM chain state plus a metadata sidecar.
+
+    Writes BUMPS native files (``<path>-chain.mc.gz`` etc. via
+    ``bumps.dream.state.save_state``) plus a ``<path>.params.json`` sidecar
+    holding parameter names and metadata. Used by :meth:`Sampler.save` and by
+    the deprecated ``easyreflectometry`` ``save_posterior`` shim.
+
+    Parameters
+    ----------
+    state : Any
+        The BUMPS ``MCMCDraw`` object to persist.
+    param_names : Optional[List[str]]
+        Parameter names (one per chain column), stored in the sidecar.
+    path : str
+        File path prefix. BUMPS appends its own suffixes.
+    data_fingerprint : Optional[str], default=None
+        SHA-256 fingerprint of the data the chain was sampled against,
+        verified (with a warning) on reload.
+    """
+    from .minimizers.minimizer_bumps import save_sampler_state
+
+    save_sampler_state(state, str(path))
+
+    sidecar = {
+        'schema_version': _SIDECAR_SCHEMA_VERSION,
+        'param_names': param_names,
+        'easyscience_version': _easyscience_version(),
+        'data_fingerprint': data_fingerprint,
+    }
+    with open(f'{path}.params.json', 'w') as f:
+        json.dump(sidecar, f, indent=2)
+
+
+def load_chain(path: str, skip: int = 0) -> tuple[Any, Optional[List[str]], dict]:
+    """Reload a DREAM chain state saved by :func:`save_chain`.
+
+    Uses the patched loader (``load_sampler_state``), which carries the
+    BUMPS >= 1.0.4 single-row ``loadtxt`` workaround. Parameter names are
+    restored from the sidecar when available (schema versions 1 and 2),
+    falling back to the state's labels with the minimizer prefix stripped.
+    Used by :meth:`Sampler.load_state` and by the deprecated
+    ``easyreflectometry`` ``load_posterior`` shim.
+
+    Parameters
+    ----------
+    path : str
+        File path prefix used when saving.
+    skip : int, default=0
+        Discard the first ``skip`` saved generations on load, forwarded to
+        ``bumps.dream.state.load_state``.
+
+    Returns
+    -------
+    tuple[Any, Optional[List[str]], dict]
+        The reloaded BUMPS ``MCMCDraw`` state, the parameter names (or
+        ``None`` if neither sidecar nor labels yielded them), and the raw
+        sidecar dict (empty if absent/unreadable).
+    """
+    from .minimizers.minimizer_bumps import load_sampler_state
+
+    state = load_sampler_state(str(path), skip=skip)
+
+    sidecar: dict = {}
+    param_names: Optional[List[str]] = None
+    try:
+        with open(f'{path}.params.json', 'r') as f:
+            sidecar = json.load(f)
+        if sidecar.get('schema_version') in _ACCEPTED_SIDECAR_SCHEMA_VERSIONS:
+            param_names = sidecar.get('param_names')
+    except (FileNotFoundError, json.JSONDecodeError):
+        sidecar = {}
+
+    if param_names is None:
+        # Fallback: strip the minimizer prefix from state.labels. Note BUMPS
+        # save_state/load_state does not preserve labels, so a reloaded state
+        # typically carries default labels like ['P0', 'P1', ...].
+        param_names = [
+            lbl[len(MINIMIZER_PARAMETER_PREFIX) :]
+            if lbl.startswith(MINIMIZER_PARAMETER_PREFIX)
+            else lbl
+            for lbl in state.labels
+        ]
+
+    return state, param_names, sidecar
+
+
+@dataclass
+class SamplingResults:
+    """Structured result of an MCMC sampling run (analogous to ``FitResults``).
+
+    Attributes
+    ----------
+    draws : np.ndarray
+        Posterior samples, shape ``(n_samples, n_params)``.
+    param_names : list[str]
+        Parameter names (one per column of ``draws``).
+    logp : np.ndarray
+        Log-posterior values, shape ``(n_samples,)``.
+    state : Any
+        The raw BUMPS ``MCMCDraw`` chain state.
+    """
+
+    draws: np.ndarray
+    param_names: list[str]
+    logp: np.ndarray
+    state: Any
+
+    def to_legacy_dict(self) -> dict:
+        """Return the legacy dict shape produced by the deprecated
+        ``mcmc_sample()`` APIs."""
+        return {
+            'draws': self.draws,
+            'param_names': self.param_names,
+            'internal_bumps_object': self.state,
+            'logp': self.logp,
+        }
+
+
+class Sampler:
+    """Bayesian MCMC sampler for one dataset, backed by a Fitter's BUMPS minimizer.
+
+    One ``Sampler`` instance represents one chain over one ``(x, y, weights)``
+    dataset. The data is bound at construction; :meth:`sample` and
+    :meth:`extend` take no data arguments, so a chain can never be extended
+    against different data (undefined behaviour in BUMPS).
+
+    Construct directly with a configured ``Fitter`` (or ``MultiFitter``) whose
+    minimizer has been switched to ``AvailableMinimizers.Bumps``.
+
+    The sampler is BUMPS/DREAM-specific for now: the BUMPS check in
+    :meth:`_run` is the seam where another backend would plug in.
+
+    Parameters
+    ----------
+    fitter : Fitter
+        A configured ``Fitter`` (or ``MultiFitter``) whose minimizer has been
+        switched to ``AvailableMinimizers.Bumps``.
+    x : Union[np.ndarray, List[np.ndarray]]
+        Independent variable array (or list of arrays for ``MultiFitter``).
+    y : Union[np.ndarray, List[np.ndarray]]
+        Dependent variable array (or list of arrays for ``MultiFitter``).
+    weights : Union[Optional[np.ndarray], List[Optional[np.ndarray]]], default=None
+        Weight array (or list of arrays for ``MultiFitter``).
+    vectorized : bool, default=False
+        When ``True``, each x array may be multi-dimensional (e.g. an
+        ``(N, M, 2)`` grid for a 2D model) and is left as-is.
+    sampler_kwargs : Optional[dict], default=None
+        Per-instance default keyword arguments forwarded to the BUMPS DREAM
+        sampler on every run, merged with (and overridden by) per-call
+        ``sampler_kwargs``.
+    """
+
+    def __init__(
+        self,
+        fitter: 'Fitter',
+        x: Union[np.ndarray, List[np.ndarray]],
+        y: Union[np.ndarray, List[np.ndarray]],
+        weights: Union[Optional[np.ndarray], List[Optional[np.ndarray]]] = None,
+        vectorized: bool = False,
+        sampler_kwargs: Optional[dict] = None,
+    ):
+        self._fitter = fitter
+        self._x = x
+        self._y = y
+        self._weights = weights
+        self._vectorized = vectorized
+        self._default_sampler_kwargs = dict(sampler_kwargs or {})
+        self._state: Any = None  # BUMPS MCMCDraw (current chain state)
+        self._results: Optional[SamplingResults] = None
+
+    @property
+    def state(self) -> Any:
+        """Raw BUMPS MCMCDraw state (None before first sample/load_state)."""
+        return self._state
+
+    @property
+    def results(self) -> Optional[SamplingResults]:
+        """Results of the most recent sample/extend/load_state call."""
+        return self._results
+
+    @property
+    def draws(self) -> Optional[np.ndarray]:
+        """Posterior draws from the most recent run (or None)."""
+        return self._results.draws if self._results is not None else None
+
+    @property
+    def param_names(self) -> Optional[List[str]]:
+        """Parameter names from the most recent run (or None)."""
+        return self._results.param_names if self._results is not None else None
+
+    @property
+    def logp(self) -> Optional[np.ndarray]:
+        """Log-posterior values from the most recent run (or None)."""
+        return self._results.logp if self._results is not None else None
+
+    def _fingerprint(self) -> Optional[str]:
+        """SHA-256 fingerprint of the bound (x, y, weights) data, or None."""
+        x_list = list(self._x) if isinstance(self._x, (list, tuple)) else [self._x]
+        y_list = list(self._y) if isinstance(self._y, (list, tuple)) else [self._y]
+        if self._weights is None:
+            w_list = []
+        elif isinstance(self._weights, (list, tuple)):
+            w_list = [w for w in self._weights if w is not None]
+        else:
+            w_list = [self._weights]
+        return _data_fingerprint(x_list, y_list, w_list)
+
+    def _run(
+        self,
+        samples: int,
+        burn: int,
+        thin: int,
+        population: Optional[int],
+        resume_state: Optional[Any],
+        sampler_kwargs: Optional[dict],
+        progress_callback: Optional[Callable[[dict], Optional[bool]]],
+        abort_test: Optional[Callable[[], bool]],
+    ) -> SamplingResults:
+        """Shared sampling engine for :meth:`sample` and :meth:`extend`.
+
+        Argument validation for ``samples``/``burn``/``thin`` lives in
+        ``Bumps.mcmc_sample`` (single source of truth).
+        """
+        # Check the minimizer is BUMPS *before* mutating the fitter — a
+        # non-BUMPS fitter must not be needlessly rebuilt.
+        minimizer = self._fitter.minimizer
+        if not (hasattr(minimizer, 'package') and minimizer.package == 'bumps'):
+            raise RuntimeError(
+                'Bayesian sampling requires a BUMPS minimizer. '
+                'Use ``fitter.switch_minimizer(AvailableMinimizers.Bumps)`` first.'
+            )
+
+        x_fit, x_new, y_new, w_new, dims = self._fitter._precompute_reshaping(
+            self._x, self._y, self._weights, self._vectorized
+        )
+        self._fitter._dependent_dims = dims
+        wrapped = self._fitter._fit_function_wrapper(x_new, flatten=True)
+
+        merged_kwargs = {**self._default_sampler_kwargs, **(sampler_kwargs or {})}
+
+        original_fit_func = self._fitter.fit_function
+        # Assigning fit_function triggers _update_minimizer() and *rebuilds*
+        # the minimizer object — it must be re-fetched after this assignment.
+        self._fitter.fit_function = wrapped
+        try:
+            minimizer = self._fitter.minimizer
+            result = minimizer.mcmc_sample(
+                x=x_fit,
+                y=y_new,
+                weights=w_new,
+                samples=samples,
+                burn=burn,
+                thin=thin,
+                population=population,
+                resume_state=resume_state,
+                sampler_kwargs=merged_kwargs or None,
+                progress_callback=progress_callback,
+                abort_test=abort_test,
+            )
+        finally:
+            self._fitter.fit_function = original_fit_func
+
+        results = SamplingResults(
+            draws=result['draws'],
+            param_names=result['param_names'],
+            logp=result['logp'],
+            state=result['internal_bumps_object'],
+        )
+        self._state = results.state
+        self._results = results
+        return results
+
+    def sample(
+        self,
+        samples: int = 10000,
+        burn: int = 2000,
+        thin: int = 10,
+        population: Optional[int] = None,
+        sampler_kwargs: Optional[dict] = None,
+        progress_callback: Optional[Callable[[dict], Optional[bool]]] = None,
+        abort_test: Optional[Callable[[], bool]] = None,
+    ) -> SamplingResults:
+        """Run fresh Bayesian MCMC sampling on the bound data.
+
+        Calling ``sample()`` on a sampler that already holds a chain starts a
+        **fresh** chain — the previous state and results are replaced. Use
+        :meth:`extend` to continue an existing chain.
+
+        Parameters
+        ----------
+        samples : int, default=10000
+            Number of retained DREAM samples requested from BUMPS.
+        burn : int, default=2000
+            Burn-in steps to discard before collecting samples.
+        thin : int, default=10
+            Thinning interval — only every ``thin``-th sample is kept,
+            which reduces autocorrelation between consecutive draws.
+        population : Optional[int], default=None
+            DREAM population **scale factor** (not an absolute chain count):
+            BUMPS creates ``ceil(population * n_parameters)`` parallel chains.
+        sampler_kwargs : Optional[dict], default=None
+            Additional keyword arguments forwarded to the BUMPS DREAM
+            sampler (merged over the instance defaults).
+        progress_callback : Optional[Callable[[dict], Optional[bool]]], default=None
+            Optional callback invoked at each DREAM generation. The payload
+            dict includes ``iteration`` and ``sampling: True``.
+        abort_test : Optional[Callable[[], bool]], default=None
+            Optional callable that returns ``True`` to abort sampling early.
+
+        Returns
+        -------
+        SamplingResults
+            Structured sampling results (also stored on :attr:`results`).
+
+        Notes
+        -----
+        Exceptions propagate from the sampling engine: ``ValueError`` if
+        ``samples``, ``burn``, or ``thin`` are invalid, and ``RuntimeError``
+        if the active minimizer is not a BUMPS instance.
+        """
+        return self._run(
+            samples=samples,
+            burn=burn,
+            thin=thin,
+            population=population,
+            resume_state=None,
+            sampler_kwargs=sampler_kwargs,
+            progress_callback=progress_callback,
+            abort_test=abort_test,
+        )
+
+    def extend(
+        self,
+        additional_samples: int = 5000,
+        thin: int = 10,
+        total_samples: Optional[int] = None,
+        sampler_kwargs: Optional[dict] = None,
+        progress_callback: Optional[Callable[[dict], Optional[bool]]] = None,
+        abort_test: Optional[Callable[[], bool]] = None,
+    ) -> SamplingResults:
+        """Continue the existing chain with additional samples.
+
+        DREAM stores draws in a fixed-size ring buffer sized to its
+        ``samples`` parameter; this method does the ring-buffer arithmetic
+        for you (``samples = stored_generations * population +
+        additional_samples``) so no existing draws are lost, regardless of
+        the thinning interval. Runs with ``burn=0`` — re-burning a converged
+        chain is usually a mistake. The DREAM population is recovered from
+        the saved state and cannot be changed on extend.
+
+        Parameters
+        ----------
+        additional_samples : int, default=5000
+            Number of additional DREAM samples to draw, in the same units
+            as ``samples`` in :meth:`sample`. With thinning, approximately
+            ``additional_samples / thin`` new draws are retained.
+        thin : int, default=10
+            Thinning interval for the retained draws.
+        total_samples : Optional[int], default=None
+            Advanced: total retained samples requested from the ring buffer,
+            **overriding** the ``additional_samples`` arithmetic. With
+            ``total_samples=N``, only the last N draws are retained.
+        sampler_kwargs : Optional[dict], default=None
+            Additional keyword arguments forwarded to the BUMPS DREAM
+            sampler (merged over the instance defaults).
+        progress_callback : Optional[Callable[[dict], Optional[bool]]], default=None
+            Optional callback invoked at each DREAM generation.
+        abort_test : Optional[Callable[[], bool]], default=None
+            Optional callable that returns ``True`` to abort sampling early.
+
+        Returns
+        -------
+        SamplingResults
+            Structured sampling results for the full (extended) chain.
+
+        Raises
+        ------
+        RuntimeError
+            If there is no chain to extend (call :meth:`sample` or
+            :meth:`load_state` first), or the minimizer is not BUMPS.
+        """
+        if self._state is None:
+            raise RuntimeError('No chain to extend. Call sample() or load_state() first.')
+        if total_samples is None:
+            # The DREAM ring buffer holds Ngen generations of Npop points
+            # each. Sizing the new buffer from the stored generations (rather
+            # than the retained-draw count, which is divided by the thinning
+            # interval) guarantees no existing draws are dropped for any
+            # ``thin`` value.
+            old_samples = int(self._state.Ngen) * int(self._state.Npop)
+            total_samples = old_samples + int(additional_samples)
+        return self._run(
+            samples=total_samples,
+            burn=0,
+            thin=thin,
+            population=None,
+            resume_state=self._state,
+            sampler_kwargs=sampler_kwargs,
+            progress_callback=progress_callback,
+            abort_test=abort_test,
+        )
+
+    def save(self, path: str) -> None:
+        """Persist the chain state and metadata to disk.
+
+        Writes BUMPS native files (``<path>-chain.mc.gz`` etc.) plus a
+        ``<path>.params.json`` sidecar with parameter names, the easyscience
+        version, and a fingerprint of the bound data (verified with a warning
+        on :meth:`load_state`).
+
+        Parameters
+        ----------
+        path : str
+            File path prefix. BUMPS appends its own suffixes.
+
+        Raises
+        ------
+        RuntimeError
+            If no chain state exists yet (call :meth:`sample` first).
+        """
+        if self._state is None:
+            raise RuntimeError('No chain state to save. Call sample() first.')
+        save_chain(
+            self._state,
+            self._results.param_names if self._results is not None else None,
+            path,
+            data_fingerprint=self._fingerprint(),
+        )
+
+    def load_state(self, path: str, skip: int = 0) -> SamplingResults:
+        """Load a previously saved chain into this sampler.
+
+        The sampler must be constructed with the same fitter and data used to
+        create the chain — :meth:`extend` then continues the saved chain. If
+        the sidecar carries a data fingerprint and it does not match this
+        sampler's bound data, a ``UserWarning`` is emitted (extending a chain
+        against different data is undefined behaviour).
+
+        Populates :attr:`state` and :attr:`results` (draws, log-posterior and
+        parameter names) from the saved chain, so summaries and
+        ``PosteriorResults.from_sampler()`` work without resampling.
+
+        Parameters
+        ----------
+        path : str
+            File path prefix used in :meth:`save`.
+        skip : int, default=0
+            Discard the first ``skip`` saved generations on load. Useful for
+            trimming additional burn-in without re-sampling.
+
+        Returns
+        -------
+        SamplingResults
+            The reloaded chain results (also stored on :attr:`results`).
+        """
+        state, param_names, sidecar = load_chain(path, skip=skip)
+
+        saved_fingerprint = sidecar.get('data_fingerprint')
+        if saved_fingerprint is not None:
+            current_fingerprint = self._fingerprint()
+            if current_fingerprint is not None and current_fingerprint != saved_fingerprint:
+                warnings.warn(
+                    'The data bound to this Sampler does not match the data '
+                    'fingerprint stored with the saved chain. Extending a '
+                    'chain against different data is undefined behaviour.',
+                    UserWarning,
+                    stacklevel=2,
+                )
+
+        _draw = state.draw()
+        results = SamplingResults(
+            draws=_draw.points,
+            param_names=param_names,
+            logp=_draw.logp,
+            state=state,
+        )
+        self._state = state
+        self._results = results
+        return results
diff --git a/tests/integration/fitting/test_multi_fitter.py b/tests/integration/fitting/test_multi_fitter.py
index e4b87ea1..52955990 100644
--- a/tests/integration/fitting/test_multi_fitter.py
+++ b/tests/integration/fitting/test_multi_fitter.py
@@ -288,213 +288,5 @@ def test_multi_fit_1D_2D(fit_engine):
         assert np.all(result.y_obs == Y[idx])
 
 
-# ---------------------------------------------------------------------------
-# Tests for MultiFitter.mcmc_sample (Bayesian MCMC via BUMPS DREAM)
-# ---------------------------------------------------------------------------
-
-
-class TestMultiFitterMcmcSample:
-    """Integration tests for ``MultiFitter.mcmc_sample``."""
-
-    def test_raises_runtime_error_when_not_bumps(self):
-        """mcmc_sample() must raise RuntimeError if the minimizer is not a BUMPS instance."""
-        sp = AbsSin(0.354, 3.05)
-        f = MultiFitter([sp], [sp])
-
-        x = np.linspace(0, 5, 50)
-        y = np.sin(x)
-        weights = np.ones_like(x)
-
-        with pytest.raises(RuntimeError, match='Bayesian sampling requires a BUMPS minimizer'):
-            f.mcmc_sample(x=[x], y=[y], weights=[weights], samples=10, burn=5, thin=1)
-
-    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_returns_expected_keys_and_shapes(self):
-        """mcmc_sample() with BUMPS should return draws, param_names, state, logp."""
-        ref_sin = AbsSin(0.2, np.pi)
-        sp = AbsSin(0.354, 3.05)
-
-        x = np.linspace(0, 5, 50)
-        y = ref_sin(x)
-        weights = np.ones_like(x)
-
-        sp.offset.fixed = False
-        sp.phase.fixed = False
-
-        f = MultiFitter([sp], [sp])
-        try:
-            f.switch_minimizer('Bumps')
-        except AttributeError:
-            pytest.skip('BUMPS is not installed')
-
-        result = f.mcmc_sample(x=[x], y=[y], weights=[weights], samples=100, burn=20, thin=2)
-
-        assert isinstance(result, dict)
-        assert 'draws' in result
-        assert 'param_names' in result
-        assert 'internal_bumps_object' in result
-        assert 'logp' in result
-
-        # draws shape: (retained_samples, n_params)
-        assert result['draws'].ndim == 2
-        assert result['draws'].shape[1] == len(result['param_names'])
-
-        # param_names should match the model's fit parameters
-        expected_pars = {p.unique_name for p in sp.get_fit_parameters()}
-        assert set(result['param_names']) == expected_pars
-
-    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_multi_dataset_returns_consistent_param_names(self):
-        """mcmc_sample() with multiple datasets should have correct param_names across all."""
-        ref_sin_1 = AbsSin(0.2, np.pi)
-        sp_sin_1 = AbsSin(0.354, 3.05)
-        sp_line = Line(0.43, 6.1)
-
-        # Link a parameter across models
-        sp_line.m.make_dependent_on(
-            dependency_expression='sp_sin1', dependency_map={'sp_sin1': sp_sin_1.offset}
-        )
-
-        x1 = np.linspace(0, 5, 50)
-        y1 = ref_sin_1(x1)
-        x2 = np.copy(x1)
-        y2 = Line(1, 4.6)(x2)
-        weights = np.ones_like(x1)
-
-        sp_sin_1.offset.fixed = False
-        sp_sin_1.phase.fixed = False
-        sp_line.c.fixed = False
-
-        f = MultiFitter([sp_sin_1, sp_line], [sp_sin_1, sp_line])
-        try:
-            f.switch_minimizer('Bumps')
-        except AttributeError:
-            pytest.skip('BUMPS is not installed')
-
-        result = f.mcmc_sample(
-            x=[x1, x2], y=[y1, y2], weights=[weights, weights], samples=100, burn=20, thin=2
-        )
-
-        # All parameters across both models should appear
-        all_params = {p.unique_name for p in sp_sin_1.get_fit_parameters()}
-        all_params |= {p.unique_name for p in sp_line.get_fit_parameters()}
-        assert set(result['param_names']) == all_params
-
-    def test_population_param_controls_chain_count(self):
-        """Passing population should succeed and produce valid draws."""
-        sp = AbsSin(0.354, 3.05)
-        sp.offset.fixed = False
-        sp.phase.fixed = False
-        f = MultiFitter([sp], [sp])
-        try:
-            f.switch_minimizer('Bumps')
-        except AttributeError:
-            pytest.skip('BUMPS is not installed')
-
-        x = np.linspace(0, 5, 50)
-        y = np.sin(x)
-        weights = np.ones_like(x)
-
-        result = f.mcmc_sample(
-            x=[x], y=[y], weights=[weights], samples=100, burn=20, thin=2, population=5
-        )
-        assert 'draws' in result
-
-    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_vectorized_2d_input_produces_valid_draws(self):
-        """mcmc_sample() with vectorized=True and 2D input should produce valid draws."""
-        sp = AbsSin2D(0.1, 1.75)
-
-        x = np.linspace(0, 5, 50)
-        X, Y = np.meshgrid(x, x)
-        x2D = np.stack((X, Y), axis=2)
-        y2D = np.abs(np.sin(X)) * np.abs(np.sin(Y))
-        weights = np.ones_like(y2D)
-
-        sp.offset.fixed = False
-        sp.phase.fixed = False
-
-        f = MultiFitter([sp], [sp])
-        try:
-            f.switch_minimizer('Bumps')
-        except AttributeError:
-            pytest.skip('BUMPS is not installed')
-
-        result = f.mcmc_sample(
-            x=[x2D], y=[y2D], weights=[weights], samples=100, burn=20, thin=2, vectorized=True
-        )
-
-        assert result['draws'].ndim == 2
-        assert result['draws'].shape[0] > 0
-        assert result['draws'].shape[1] == len(result['param_names'])
-
-    def test_fit_function_restored_after_runtime_error(self):
-        """fit_function must be restored to its original value even when mcmc_sample() raises."""
-        sp = AbsSin(0.354, 3.05)
-        f = MultiFitter([sp], [sp])
-
-        x = np.linspace(0, 5, 50)
-        y = np.sin(x)
-        weights = np.ones_like(x)
-
-        original_func = f.fit_function
-
-        with pytest.raises(RuntimeError):
-            f.mcmc_sample(x=[x], y=[y], weights=[weights], samples=10, burn=5, thin=1)
-
-        assert f.fit_function is original_func
-
-    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_fit_function_restored_after_successful_sample(self):
-        """fit_function must be restored to its original value after a successful mcmc_sample()."""
-        ref_sin = AbsSin(0.2, np.pi)
-        sp = AbsSin(0.354, 3.05)
-
-        x = np.linspace(0, 5, 50)
-        y = ref_sin(x)
-        weights = np.ones_like(x)
-
-        sp.offset.fixed = False
-        sp.phase.fixed = False
-
-        f = MultiFitter([sp], [sp])
-        try:
-            f.switch_minimizer('Bumps')
-        except AttributeError:
-            pytest.skip('BUMPS is not installed')
-
-        original_func = f.fit_function
-        f.mcmc_sample(x=[x], y=[y], weights=[weights], samples=100, burn=20, thin=2)
-        assert f.fit_function is original_func
-
-    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_sampler_kwargs_forwarded(self):
-        """sampler_kwargs dict is forwarded to the BUMPS DREAM sampler."""
-        ref_sin = AbsSin(0.2, np.pi)
-        sp = AbsSin(0.354, 3.05)
-
-        x = np.linspace(0, 5, 50)
-        y = ref_sin(x)
-        weights = np.ones_like(x)
-
-        sp.offset.fixed = False
-        sp.phase.fixed = False
-
-        f = MultiFitter([sp], [sp])
-        try:
-            f.switch_minimizer('Bumps')
-        except AttributeError:
-            pytest.skip('BUMPS is not installed')
-
-        result = f.mcmc_sample(
-            x=[x],
-            y=[y],
-            weights=[weights],
-            samples=100,
-            burn=20,
-            thin=2,
-            sampler_kwargs={'init': 'random'},
-        )
-
-        assert result['draws'].ndim == 2
-        assert result['draws'].shape[0] > 0
+# NOTE: The Bayesian MCMC tests formerly here (TestMultiFitterMcmcSample) were
+# moved to test_sampler.py and adapted to ``Sampler(f, ...)``.
diff --git a/tests/integration/fitting/test_sampler.py b/tests/integration/fitting/test_sampler.py
new file mode 100644
index 00000000..71581514
--- /dev/null
+++ b/tests/integration/fitting/test_sampler.py
@@ -0,0 +1,454 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Integration tests for the ``Sampler`` class (Bayesian MCMC via BUMPS DREAM).
+
+The behavioural MCMC tests formerly in
+``test_multi_fitter.py::TestMultiFitterMcmcSample`` were moved here and
+adapted to ``Sampler(f,  ...)`` directly.
+"""
+
+import json
+
+import numpy as np
+import pytest
+
+from easyscience import ObjBase
+from easyscience import Parameter
+from easyscience.fitting import Sampler
+from easyscience.fitting import SamplingResults
+from easyscience.fitting.multi_fitter import MultiFitter
+
+
+class Line(ObjBase):
+    m: Parameter
+    c: Parameter
+
+    def __init__(self, m_val: float, c_val: float):
+        m = Parameter('m', m_val)
+        c = Parameter('c', c_val)
+        super(Line, self).__init__('line', m=m, c=c)
+
+    def __call__(self, x):
+        return self.m.value * x + self.c.value
+
+
+class AbsSin(ObjBase):
+    phase: Parameter
+    offset: Parameter
+
+    def __init__(self, offset_val: float, phase_val: float):
+        offset = Parameter('offset', offset_val)
+        phase = Parameter('phase', phase_val)
+        super().__init__('sin', offset=offset, phase=phase)
+
+    def __call__(self, x):
+        return np.abs(np.sin(self.phase.value * x + self.offset.value))
+
+
+class AbsSin2D(ObjBase):
+    phase: Parameter
+    offset: Parameter
+
+    def __init__(self, offset_val: float, phase_val: float):
+        offset = Parameter('offset', offset_val)
+        phase = Parameter('phase', phase_val)
+        super().__init__('sin2D', offset=offset, phase=phase)
+
+    def __call__(self, x):
+        X = x[:, :, 0]  # x is a 2D array
+        Y = x[:, :, 1]
+        return np.abs(np.sin(self.phase.value * X + self.offset.value)) * np.abs(
+            np.sin(self.phase.value * Y + self.offset.value)
+        )
+
+
+def _bumps_fitter_and_data():
+    """Build a 2-parameter BUMPS MultiFitter over a small sine model."""
+    ref_sin = AbsSin(0.2, np.pi)
+    sp = AbsSin(0.354, 3.05)
+    sp.offset.fixed = False
+    sp.phase.fixed = False
+    x = np.linspace(0, 5, 50)
+    y = ref_sin(x)
+    weights = np.ones_like(x)
+    f = MultiFitter([sp], [sp])
+    try:
+        f.switch_minimizer('Bumps')
+    except AttributeError:
+        pytest.skip('BUMPS is not installed')
+    return f, sp, x, y, weights
+
+
+class TestSampler:
+    """Integration tests for ``Sampler(f, ...)`` / ``Sampler``."""
+
+    def test_sample_requires_bumps(self):
+        """sample() must raise RuntimeError if the minimizer is not BUMPS —
+        and must not mutate the fitter (no needless minimizer rebuild)."""
+        sp = AbsSin(0.354, 3.05)
+        f = MultiFitter([sp], [sp])
+
+        x = np.linspace(0, 5, 50)
+        y = np.sin(x)
+        weights = np.ones_like(x)
+
+        sampler = Sampler(f, [x], [y], [weights])
+        minimizer_before = f.minimizer
+        with pytest.raises(RuntimeError, match='Bayesian sampling requires a BUMPS minimizer'):
+            sampler.sample(samples=10, burn=5, thin=1)
+        assert f.minimizer is minimizer_before
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_sample_returns_results_object(self):
+        """sample() returns a populated SamplingResults; to_legacy_dict() has the legacy shape."""
+        f, sp, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        results = sampler.sample(samples=100, burn=20, thin=2)
+
+        assert isinstance(results, SamplingResults)
+        assert results.draws.ndim == 2
+        assert results.draws.shape[1] == len(results.param_names)
+        assert results.logp.shape[0] == results.draws.shape[0]
+        assert results.state is not None
+
+        # param_names should match the model's fit parameters
+        expected_pars = {p.unique_name for p in sp.get_fit_parameters()}
+        assert set(results.param_names) == expected_pars
+
+        # results cached on the sampler
+        assert sampler.results is results
+        assert sampler.state is results.state
+        assert sampler.draws is results.draws
+        assert sampler.param_names == results.param_names
+
+        # legacy dict shape
+        legacy = results.to_legacy_dict()
+        assert set(legacy.keys()) == {'draws', 'param_names', 'internal_bumps_object', 'logp'}
+        assert legacy['internal_bumps_object'] is results.state
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_sample_multi_dataset(self):
+        """Multi-dataset sampling via Sampler(f, ...) has correct param_names."""
+        ref_sin_1 = AbsSin(0.2, np.pi)
+        sp_sin_1 = AbsSin(0.354, 3.05)
+        sp_line = Line(0.43, 6.1)
+
+        # Link a parameter across models
+        sp_line.m.make_dependent_on(
+            dependency_expression='sp_sin1', dependency_map={'sp_sin1': sp_sin_1.offset}
+        )
+
+        x1 = np.linspace(0, 5, 50)
+        y1 = ref_sin_1(x1)
+        x2 = np.copy(x1)
+        y2 = Line(1, 4.6)(x2)
+        weights = np.ones_like(x1)
+
+        sp_sin_1.offset.fixed = False
+        sp_sin_1.phase.fixed = False
+        sp_line.c.fixed = False
+
+        f = MultiFitter([sp_sin_1, sp_line], [sp_sin_1, sp_line])
+        try:
+            f.switch_minimizer('Bumps')
+        except AttributeError:
+            pytest.skip('BUMPS is not installed')
+
+        sampler = Sampler(f, [x1, x2], [y1, y2], [weights, weights])
+        results = sampler.sample(samples=100, burn=20, thin=2)
+
+        # All parameters across both models should appear
+        all_params = {p.unique_name for p in sp_sin_1.get_fit_parameters()}
+        all_params |= {p.unique_name for p in sp_line.get_fit_parameters()}
+        assert set(results.param_names) == all_params
+
+    def test_sample_population(self):
+        """Passing population should succeed and produce valid draws."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        results = sampler.sample(samples=100, burn=20, thin=2, population=5)
+        assert results.draws.shape[0] > 0
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_sample_vectorized_2d(self):
+        """sample() with vectorized=True and 2D input should produce valid draws."""
+        sp = AbsSin2D(0.1, 1.75)
+
+        x = np.linspace(0, 5, 50)
+        X, Y = np.meshgrid(x, x)
+        x2D = np.stack((X, Y), axis=2)
+        y2D = np.abs(np.sin(X)) * np.abs(np.sin(Y))
+        weights = np.ones_like(y2D)
+
+        sp.offset.fixed = False
+        sp.phase.fixed = False
+
+        f = MultiFitter([sp], [sp])
+        try:
+            f.switch_minimizer('Bumps')
+        except AttributeError:
+            pytest.skip('BUMPS is not installed')
+
+        sampler = Sampler(f, [x2D], [y2D], [weights], vectorized=True)
+        results = sampler.sample(samples=100, burn=20, thin=2)
+
+        assert results.draws.ndim == 2
+        assert results.draws.shape[0] > 0
+        assert results.draws.shape[1] == len(results.param_names)
+
+    def test_fit_function_restored_on_error(self):
+        """fit_function must be restored even when the minimizer raises."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        original_func = f.fit_function
+
+        # Invalid `samples` is rejected by the minimizer (single source of
+        # validation) *after* the fitter has been mutated for sampling.
+        with pytest.raises(ValueError, match='samples must be a positive integer'):
+            sampler.sample(samples=-1, burn=5, thin=1)
+
+        assert f.fit_function is original_func
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_fit_function_restored_on_success(self):
+        """fit_function must be restored after a successful sample()."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        original_func = f.fit_function
+
+        sampler.sample(samples=100, burn=20, thin=2)
+        assert f.fit_function is original_func
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_sampler_kwargs_forwarded(self):
+        """Per-call sampler_kwargs dict is forwarded to the BUMPS DREAM sampler."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        results = sampler.sample(samples=100, burn=20, thin=2, sampler_kwargs={'init': 'random'})
+
+        assert results.draws.ndim == 2
+        assert results.draws.shape[0] > 0
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_default_sampler_kwargs_merged(self):
+        """Constructor-level sampler_kwargs defaults are used; per-call kwargs win."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights], sampler_kwargs={'init': 'random'})
+
+        captured = {}
+        original_mcmc_sample = type(f.minimizer).mcmc_sample
+
+        def spy(self, **kwargs):
+            captured.update(kwargs.get('sampler_kwargs') or {})
+            return original_mcmc_sample(self, **kwargs)
+
+        try:
+            type(f.minimizer).mcmc_sample = spy
+            sampler.sample(samples=100, burn=20, thin=2)
+            assert captured == {'init': 'random'}
+
+            captured.clear()
+            sampler.sample(samples=100, burn=20, thin=2, sampler_kwargs={'init': 'lhs'})
+            assert captured == {'init': 'lhs'}  # per-call overrides default
+        finally:
+            type(f.minimizer).mcmc_sample = original_mcmc_sample
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_extend_chain(self):
+        """extend(additional_samples=) continues the chain; ring-buffer math is done for the user."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        first = sampler.sample(samples=100, burn=20, thin=1)
+        n_first = first.draws.shape[0]
+
+        extended = sampler.extend(additional_samples=100, thin=1)
+
+        assert extended.draws.shape[1] == first.draws.shape[1]
+        assert extended.draws.shape[0] >= n_first
+        assert sampler.results is extended
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_extend_with_thinning_keeps_existing_draws(self):
+        """extend() must not drop stored draws when thin > 1.
+
+        Regression test: the ring buffer is sized from the state's stored
+        generations (``Ngen * Npop``), not from the retained-draw count,
+        which BUMPS divides by the thinning interval.
+        """
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        first = sampler.sample(samples=1000, burn=20, thin=10)
+        n_first = first.draws.shape[0]
+
+        extended = sampler.extend(additional_samples=1000, thin=10)
+
+        # All previously retained draws are kept, plus ~additional/thin new ones.
+        assert extended.draws.shape[0] > n_first
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_extend_total_samples_override(self):
+        """extend(total_samples=) bypasses the additional_samples arithmetic."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        sampler.sample(samples=100, burn=20, thin=1)
+        extended = sampler.extend(total_samples=150, thin=1)
+
+        assert extended.draws.shape[0] > 0
+
+    def test_extend_requires_existing_state(self):
+        """extend() before sample()/load_state() raises RuntimeError."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        with pytest.raises(RuntimeError, match='No chain to extend'):
+            sampler.extend(additional_samples=10)
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_extend_after_save_load_roundtrip(self, tmp_path, caplog):
+        """save() -> new sampler -> load_state() -> extend() continues the chain.
+
+        Regression coverage: BUMPS ``load_state`` does not preserve parameter
+        labels, so resume validation must proceed by parameter order (with a
+        logged warning) instead of raising.
+        """
+        import logging
+
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        first = sampler.sample(samples=100, burn=20, thin=1)
+
+        prefix = str(tmp_path / 'chain')
+        sampler.save(prefix)
+
+        sampler2 = Sampler(f, [x], [y], [weights])
+        loaded = sampler2.load_state(prefix)
+        assert loaded.draws.shape[1] == first.draws.shape[1]
+
+        with caplog.at_level(logging.WARNING, logger='easyscience.fitting.bumps'):
+            extended = sampler2.extend(additional_samples=100, thin=1)
+
+        assert 'does not carry parameter names' in caplog.text
+        assert extended.draws.shape[1] == first.draws.shape[1]
+        assert extended.draws.shape[0] > 0
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_extend_preserves_nondefault_population(self):
+        """Extending a chain that used a non-default population must not fail.
+
+        Regression test: the population scale factor is recovered from the
+        saved state on resume, otherwise BUMPS regenerates the default
+        population and raises ``Cannot change Nvar, Npop or Ncr on resize``.
+        """
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        first = sampler.sample(samples=100, burn=20, thin=1, population=5)
+        first_npop = first.state.Npop
+
+        extended = sampler.extend(additional_samples=100, thin=1)
+
+        assert extended.draws.shape[1] == first.draws.shape[1]
+        assert extended.draws.shape[0] > 0
+        # Population must be preserved across the extend.
+        assert extended.state.Npop == first_npop
+
+    def test_save_raises_without_state(self, tmp_path):
+        """save() before sample() raises RuntimeError."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+
+        with pytest.raises(RuntimeError, match='No chain state to save'):
+            sampler.save(str(tmp_path / 'chain'))
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_load_state_populates_results(self, tmp_path):
+        """A freshly loaded sampler reports draws/logp/param_names without resampling."""
+        f, sp, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        first = sampler.sample(samples=100, burn=20, thin=2)
+
+        prefix = str(tmp_path / 'chain')
+        sampler.save(prefix)
+
+        sampler2 = Sampler(f, [x], [y], [weights])
+        assert sampler2.draws is None
+        loaded = sampler2.load_state(prefix)
+
+        assert sampler2.state is loaded.state
+        assert loaded.draws.ndim == 2
+        assert loaded.draws.shape[1] == first.draws.shape[1]
+        assert loaded.logp.shape[0] == loaded.draws.shape[0]
+        # Sidecar restores the original (prefix-stripped) parameter names.
+        assert loaded.param_names == first.param_names
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_load_restores_param_names_from_sidecar(self, tmp_path):
+        """v2 sidecar restores names; a legacy v1 sidecar is still accepted."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        first = sampler.sample(samples=100, burn=20, thin=2)
+
+        prefix = str(tmp_path / 'chain')
+        sampler.save(prefix)
+
+        # v2 sidecar written by Sampler.save()
+        with open(f'{prefix}.params.json') as fh:
+            sidecar = json.load(fh)
+        assert sidecar['schema_version'] == 2
+        assert sidecar['param_names'] == first.param_names
+        assert sidecar['data_fingerprint'] is not None
+
+        # Rewrite as a legacy v1 sidecar (as written by old save_posterior)
+        with open(f'{prefix}.params.json', 'w') as fh:
+            json.dump(
+                {
+                    'schema_version': 1,
+                    'param_names': first.param_names,
+                    'easyreflectometry_version': '0.0.0',
+                },
+                fh,
+            )
+
+        sampler2 = Sampler(f, [x], [y], [weights])
+        loaded = sampler2.load_state(prefix)
+        assert loaded.param_names == first.param_names
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_load_short_chain_regression(self, tmp_path):
+        """Short chain save/load — pins the BUMPS >= 1.0.4 loadtxt workaround.
+
+        A short chain stores a single CR-weight update row; BUMPS' numpy-based
+        reader collapses it to a 1-D array and ``load_state`` raises
+        ``IndexError`` without the 2-D coercion workaround.
+        """
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        sampler.sample(samples=20, burn=5, thin=1)
+
+        prefix = str(tmp_path / 'short_chain')
+        sampler.save(prefix)
+
+        sampler2 = Sampler(f, [x], [y], [weights])
+        loaded = sampler2.load_state(prefix)
+        assert loaded.draws.shape[0] > 0
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_load_fingerprint_mismatch_warns(self, tmp_path):
+        """Loading a chain into a sampler bound to different data warns."""
+        f, _, x, y, weights = _bumps_fitter_and_data()
+        sampler = Sampler(f, [x], [y], [weights])
+        sampler.sample(samples=100, burn=20, thin=2)
+
+        prefix = str(tmp_path / 'chain')
+        sampler.save(prefix)
+
+        other_y = y + 0.5
+        sampler2 = Sampler(f, [x], [other_y], [weights])
+        with pytest.warns(UserWarning, match='does not match the data fingerprint'):
+            sampler2.load_state(prefix)
diff --git a/tests/unit/fitting/minimizers/test_minimizer_bumps.py b/tests/unit/fitting/minimizers/test_minimizer_bumps.py
index 09ed68fc..81ba2ec2 100644
--- a/tests/unit/fitting/minimizers/test_minimizer_bumps.py
+++ b/tests/unit/fitting/minimizers/test_minimizer_bumps.py
@@ -750,8 +750,13 @@ def _setup_driver_mock(
             mock_driver.fitter.state = None
         else:
             mock_state = MagicMock()
-            mock_state.draw.return_value.points = np.array([[1.0]])
-            mock_state.logp = None
+            mock_state.Nvar = 1
+            mock_state.Npop = 5
+            mock_state.labels = ['p_param_0']
+            mock_draw = MagicMock()
+            mock_draw.points = np.array([[1.0]])
+            mock_draw.logp = np.array([0.5])
+            mock_state.draw.return_value = mock_draw
             mock_driver.fitter.state = mock_state
 
         mock_FitDriver = MagicMock(return_value=mock_driver)
@@ -760,6 +765,31 @@ def _setup_driver_mock(
         )
         return mock_FitDriver, mock_driver
 
+    @pytest.mark.parametrize(
+        'kwargs, match',
+        [
+            ({'samples': 0}, 'samples must be a positive integer'),
+            ({'samples': -1}, 'samples must be a positive integer'),
+            ({'burn': -1}, 'burn must be a non-negative integer'),
+            ({'thin': 0}, 'thin must be a positive integer'),
+        ],
+    )
+    def test_sample_invalid_args(self, minimizer: Bumps, kwargs, match) -> None:
+        """Invalid samples/burn/thin values raise ValueError before any sampling.
+
+        This is the single source of truth for these checks — the higher-level
+        ``Sampler`` relies on it.
+        """
+        with pytest.raises(ValueError, match=match):
+            minimizer.mcmc_sample(
+                x=np.array([1.0]),
+                y=np.array([0.1]),
+                weights=np.array([1.0]),
+                samples=kwargs.get('samples', 10),
+                burn=kwargs.get('burn', 0),
+                thin=kwargs.get('thin', 1),
+            )
+
     def test_sample_basic(self, minimizer: Bumps, monkeypatch) -> None:
         """Verify that mcmc_sample() returns a dict with expected keys."""
         mock_FitDriver, _ = self._setup_driver_mock(monkeypatch)
@@ -860,6 +890,177 @@ def test_sample_rejects_non_callable_callback(self, minimizer: Bumps, monkeypatc
                 progress_callback='not-callable',
             )
 
+    # --- Resume-state tests -------------------------------------------------
+
+    def _make_resume_state_mock(self, *, nvar=2, npop=10, labels=None):
+        """Build a mock MCMCDraw for resume tests.
+
+        BUMPS labels follow the pattern ``'p<param_name>'`` (the
+        ``MINIMIZER_PARAMETER_PREFIX`` concatenated with the unique name),
+        e.g. ``'pFilm_thickness'``.
+
+        :param nvar: Number of parameters.
+        :param npop: Population size.
+        :param labels: Parameter labels (defaults to ``['pa', 'pb']``
+            which strip to ``['a', 'b']``).
+        """
+        if labels is None:
+            labels = ['pa', 'pb']
+        mock_state = MagicMock()
+        mock_state.Nvar = nvar
+        mock_state.Npop = npop
+        mock_state.labels = labels
+        mock_draw = MagicMock()
+        mock_draw.points = np.ones((20, nvar))
+        mock_draw.logp = np.ones(20)
+        mock_state.draw.return_value = mock_draw
+        return mock_state
+
+    def _make_problem_with_parameters(self, param_names):
+        """Build a mock FitProblem whose ``_parameters`` yields the given names."""
+        params = []
+        for name in param_names:
+            p = MagicMock()
+            p.name = 'p' + name
+            params.append(p)
+        mock_problem = MagicMock()
+        mock_problem._parameters = params
+        return mock_problem
+
+    def test_sample_resume_state(self, minimizer: Bumps, monkeypatch) -> None:
+        """Verify resume_state is forwarded to driver.fit()."""
+        mock_FitDriver, mock_driver = self._setup_driver_mock(monkeypatch)
+        minimizer._make_model = MagicMock(return_value=MagicMock(return_value=MagicMock()))
+        resume_state = self._make_resume_state_mock()
+
+        import bumps.names
+
+        monkeypatch.setattr(
+            bumps.names,
+            'FitProblem',
+            MagicMock(return_value=self._make_problem_with_parameters(['a', 'b'])),
+        )
+
+        result = minimizer.mcmc_sample(
+            x=np.array([1.0, 2.0]),
+            y=np.array([0.1, 0.2]),
+            weights=np.array([1.0, 1.0]),
+            samples=10,
+            burn=0,
+            thin=1,
+            resume_state=resume_state,
+        )
+
+        assert result is not None
+        # Verify a fit_state (defensive copy of resume_state) was passed to driver.fit()
+        call_kwargs = mock_driver.fit.call_args.kwargs
+        assert call_kwargs.get('fit_state') is not None
+        assert call_kwargs['fit_state'] is not resume_state
+
+    def test_sample_resume_param_mismatch_raises(self, minimizer: Bumps, monkeypatch) -> None:
+        """Parameter count mismatch raises ValueError before driver.fit()."""
+        import bumps.names
+
+        monkeypatch.setattr(
+            bumps.names,
+            'FitProblem',
+            MagicMock(return_value=self._make_problem_with_parameters(['a'])),
+        )
+        minimizer._make_model = MagicMock(return_value=MagicMock(return_value=MagicMock()))
+        # resume_state has 2 params, model has 1
+        resume_state = self._make_resume_state_mock(nvar=2)
+
+        with pytest.raises(ValueError, match='resume_state has 2 parameters'):
+            minimizer.mcmc_sample(
+                x=np.array([1.0]),
+                y=np.array([0.1]),
+                weights=np.array([1.0]),
+                samples=10,
+                burn=0,
+                thin=1,
+                resume_state=resume_state,
+            )
+
+    def test_sample_resume_param_name_mismatch_raises(self, minimizer: Bumps, monkeypatch) -> None:
+        """Parameter name/order mismatch raises ValueError before driver.fit()."""
+        import bumps.names
+
+        monkeypatch.setattr(
+            bumps.names,
+            'FitProblem',
+            MagicMock(return_value=self._make_problem_with_parameters(['a', 'b'])),
+        )
+        minimizer._make_model = MagicMock(return_value=MagicMock(return_value=MagicMock()))
+        # resume_state has labels ['px', 'py'] → stripped to ['x', 'y']
+        # Current model has params ['pa', 'pb'] → stripped to ['a', 'b']
+        # → mismatch
+        resume_state = self._make_resume_state_mock(nvar=2, labels=['px', 'py'])
+
+        with pytest.raises(ValueError, match='Parameter names/order mismatch'):
+            minimizer.mcmc_sample(
+                x=np.array([1.0]),
+                y=np.array([0.1]),
+                weights=np.array([1.0]),
+                samples=10,
+                burn=0,
+                thin=1,
+                resume_state=resume_state,
+            )
+
+    def test_sample_resume_population_mismatch_raises(self, minimizer: Bumps, monkeypatch) -> None:
+        """Explicit population differing from state.Npop raises ValueError."""
+        import bumps.names
+
+        monkeypatch.setattr(
+            bumps.names,
+            'FitProblem',
+            MagicMock(return_value=self._make_problem_with_parameters(['a', 'b'])),
+        )
+        minimizer._make_model = MagicMock(return_value=MagicMock(return_value=MagicMock()))
+        resume_state = self._make_resume_state_mock(nvar=2, npop=10)
+
+        with pytest.raises(ValueError, match='would produce'):
+            minimizer.mcmc_sample(
+                x=np.array([1.0]),
+                y=np.array([0.1]),
+                weights=np.array([1.0]),
+                samples=10,
+                burn=0,
+                thin=1,
+                population=3,  # ceil(3*2)=6 ≠ 10
+                resume_state=resume_state,
+            )
+
+    def test_sample_resume_forces_burn_to_zero(
+        self, minimizer: Bumps, monkeypatch, caplog: 'pytest.LogCaptureFixture'
+    ) -> None:
+        """burn>0 with resume_state warns and is forced to 0."""
+        mock_FitDriver, _ = self._setup_driver_mock(monkeypatch)
+        import bumps.names
+
+        monkeypatch.setattr(
+            bumps.names,
+            'FitProblem',
+            MagicMock(return_value=self._make_problem_with_parameters(['a', 'b'])),
+        )
+        minimizer._make_model = MagicMock(return_value=MagicMock(return_value=MagicMock()))
+        resume_state = self._make_resume_state_mock()
+
+        with caplog.at_level(logging.WARNING, logger='easyscience.fitting.bumps'):
+            minimizer.mcmc_sample(
+                x=np.array([1.0]),
+                y=np.array([0.1]),
+                weights=np.array([1.0]),
+                samples=10,
+                burn=5,
+                thin=1,
+                resume_state=resume_state,
+            )
+
+        assert 'ignored on resume' in caplog.text
+        # burn must be forced to 0 in the kwargs passed to BUMPS
+        assert mock_FitDriver.call_args.kwargs['burn'] == 0
+
 
 # ===================================================================
 # _build_sample_progress_payload
diff --git a/tests/unit/fitting/test_fitter.py b/tests/unit/fitting/test_fitter.py
index 634492c5..702f5e59 100644
--- a/tests/unit/fitting/test_fitter.py
+++ b/tests/unit/fitting/test_fitter.py
@@ -258,156 +258,3 @@ def test_post_compute_reshaping(self, fitter: Fitter):
 # TODO
 #       def test_fit_function_wrapper()
 #       def test_precompute_reshaping()
-
-
-# ===================================================================
-# Fitter.mcmc_sample() — Bayesian DREAM sampling
-# ===================================================================
-
-
-class TestFitterMcmcSample:
-    @pytest.fixture
-    def fitter(self, monkeypatch):
-        monkeypatch.setattr(Fitter, '_update_minimizer', MagicMock())
-        return Fitter(MagicMock(), MagicMock())
-
-    def test_basic(self, fitter: Fitter):
-        """mcmc_sample() calls minimizer.mcmc_sample() and returns its result."""
-        fitter._precompute_reshaping = MagicMock(
-            return_value=('x_fit', 'x_new', 'y_new', 'w_new', 'dims')
-        )
-        fitter._fit_function_wrapper = MagicMock(return_value='wrapped')
-        fitter._minimizer = MagicMock()
-        fitter._minimizer.package = 'bumps'
-        expected = {
-            'draws': np.array([[1.0]]),
-            'param_names': ['a'],
-            'internal_bumps_object': 'stub',
-            'logp': None,
-        }
-        fitter._minimizer.mcmc_sample = MagicMock(return_value=expected)
-
-        result = fitter.mcmc_sample(
-            x=np.array([1.0]),
-            y=np.array([0.1]),
-            weights=np.array([1.0]),
-            samples=100,
-            burn=20,
-            thin=2,
-            population=5,
-        )
-
-        assert result == expected
-        fitter._precompute_reshaping.assert_called_once_with(
-            np.array([1.0]), np.array([0.1]), np.array([1.0]), False
-        )
-        fitter._fit_function_wrapper.assert_called_once_with('x_new', flatten=True)
-        fitter._minimizer.mcmc_sample.assert_called_once()
-        kw = fitter._minimizer.mcmc_sample.call_args.kwargs
-        assert kw['x'] == 'x_fit'
-        assert kw['y'] == 'y_new'
-        assert kw['weights'] == 'w_new'
-        assert kw['samples'] == 100
-        assert kw['burn'] == 20
-        assert kw['thin'] == 2
-        assert kw['population'] == 5
-        assert kw['progress_callback'] is None
-        assert fitter._dependent_dims == 'dims'
-
-    def test_raises_if_not_bumps(self, fitter: Fitter):
-        """RuntimeError raised when the active minimizer is not BUMPS."""
-        fitter._precompute_reshaping = MagicMock(
-            return_value=('x_fit', 'x_new', 'y_new', 'w_new', 'dims')
-        )
-        fitter._fit_function_wrapper = MagicMock(return_value='wrapped')
-        fitter._minimizer = MagicMock()
-        fitter._minimizer.package = 'lmfit'
-
-        with pytest.raises(RuntimeError, match='Bayesian sampling requires a BUMPS minimizer'):
-            fitter.mcmc_sample(x=np.array([1.0]), y=np.array([0.1]), weights=np.array([1.0]))
-
-    def test_progress_callback_forwarded(self, fitter: Fitter):
-        """progress_callback is forwarded to minimizer.mcmc_sample()."""
-        fitter._precompute_reshaping = MagicMock(
-            return_value=('x_fit', 'x_new', 'y_new', 'w_new', 'dims')
-        )
-        fitter._fit_function_wrapper = MagicMock(return_value='wrapped')
-        fitter._minimizer = MagicMock()
-        fitter._minimizer.package = 'bumps'
-        fitter._minimizer.mcmc_sample = MagicMock(
-            return_value={
-                'draws': [],
-                'param_names': [],
-                'internal_bumps_object': None,
-                'logp': None,
-            }
-        )
-        cb = MagicMock()
-
-        fitter.mcmc_sample(
-            x=np.array([1.0]),
-            y=np.array([0.1]),
-            weights=np.array([1.0]),
-            progress_callback=cb,
-        )
-
-        assert fitter._minimizer.mcmc_sample.call_args.kwargs['progress_callback'] is cb
-
-    def test_fit_function_restored_on_success(self, fitter: Fitter):
-        """Original fit function is restored after a successful call."""
-        fitter._precompute_reshaping = MagicMock(
-            return_value=('x_fit', 'x_new', 'y_new', 'w_new', 'dims')
-        )
-        fitter._fit_function_wrapper = MagicMock(return_value='wrapped')
-        fitter._minimizer = MagicMock()
-        fitter._minimizer.package = 'bumps'
-        fitter._minimizer.mcmc_sample = MagicMock(
-            return_value={
-                'draws': [],
-                'param_names': [],
-                'internal_bumps_object': None,
-                'logp': None,
-            }
-        )
-        original = fitter._fit_function
-
-        fitter.mcmc_sample(x=np.array([1.0]), y=np.array([0.1]), weights=np.array([1.0]))
-
-        assert fitter._fit_function is original
-
-    def test_fit_function_restored_on_error(self, fitter: Fitter):
-        """Original fit function is restored even when minimizer raises."""
-        fitter._precompute_reshaping = MagicMock(
-            return_value=('x_fit', 'x_new', 'y_new', 'w_new', 'dims')
-        )
-        fitter._fit_function_wrapper = MagicMock(return_value='wrapped')
-        fitter._minimizer = MagicMock()
-        fitter._minimizer.package = 'bumps'
-        fitter._minimizer.mcmc_sample = MagicMock(side_effect=RuntimeError('boom'))
-        original = fitter._fit_function
-
-        with pytest.raises(RuntimeError):
-            fitter.mcmc_sample(x=np.array([1.0]), y=np.array([0.1]), weights=np.array([1.0]))
-
-        assert fitter._fit_function is original
-
-    @pytest.mark.parametrize(
-        'kwargs, match',
-        [
-            ({'samples': 0}, 'samples must be a positive integer'),
-            ({'samples': -1}, 'samples must be a positive integer'),
-            ({'burn': -1}, 'burn must be a non-negative integer'),
-            ({'thin': 0}, 'thin must be a positive integer'),
-        ],
-    )
-    def test_invalid_args_raise(self, fitter: Fitter, kwargs, match):
-        """Invalid samples/burn/thin values raise ValueError before any I/O."""
-        with pytest.raises(ValueError, match=match):
-            fitter.mcmc_sample(
-                x=np.array([1.0]),
-                y=np.array([0.1]),
-                weights=np.array([1.0]),
-                samples=kwargs.get('samples', 10),
-                burn=kwargs.get('burn', 0),
-                thin=kwargs.get('thin', 1),
-            )