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   "source": [
    "(sampler_stats)=\n",
    "# Sampler Statistics\n",
    "\n",
    ":::{post} May 31, 2022\n",
    ":tags: diagnostics\n",
    ":category: beginner\n",
    ":author: Meenal Jhajharia, Christian Luhmann\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC v5.28.0+58.gf58491a3\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import pymc as pm\n",
    "\n",
    "print(f\"Running on PyMC v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.style.use(\"arviz-variat\")\n",
    "plt.rcParams[\"figure.constrained_layout.use\"] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When checking for convergence or when debugging a badly behaving sampler, it is often helpful to take a closer look at what the sampler is doing. For this purpose some samplers export statistics for each generated sample.\n",
    "\n",
    "As a minimal example we sample from a standard normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pm.Model()\n",
    "with model:\n",
    "    mu1 = pm.Normal(\"mu1\", mu=0, sigma=1, shape=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [mu1]\n"
     ]
    },
    {
     "data": {
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       "model_id": "e859b236e88f458ca4908723a5b52c76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
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    },
    {
     "data": {
      "text/html": [
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 6 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    step = pm.NUTS()\n",
    "    idata = pm.sample(2000, tune=1000, init=None, step=step, chains=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `Note`: NUTS provides the following statistics (these are internal statistics that the sampler uses, you don't need to do anything with them when using PyMC, to learn more about them, {class}`pymc.NUTS`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree &#x27;sample_stats&#x27;&gt;\n",
       "Group: /sample_stats\n",
       "    Dimensions:                (chain: 4, draw: 2000)\n",
       "    Coordinates:\n",
       "      * chain                  (chain) int64 32B 0 1 2 3\n",
       "      * draw                   (draw) int64 16kB 0 1 2 3 4 ... 1996 1997 1998 1999\n",
       "    Data variables: (12/18)\n",
       "        acceptance_rate        (chain, draw) float64 64kB 0.5117 0.8243 ... 0.6885\n",
       "        n_steps                (chain, draw) float64 64kB 3.0 7.0 7.0 ... 7.0 7.0\n",
       "        max_energy_error       (chain, draw) float64 64kB 0.9907 0.5716 ... 0.7114\n",
       "        divergences            (chain, draw) int64 64kB 0 0 0 0 0 0 ... 0 0 0 0 0 0\n",
       "        index_in_trajectory    (chain, draw) int64 64kB -3 -4 -5 -3 3 ... 2 3 -4 6 4\n",
       "        energy                 (chain, draw) float64 64kB 22.85 22.25 ... 17.12\n",
       "        ...                     ...\n",
       "        tree_depth             (chain, draw) int64 64kB 2 3 3 3 3 3 ... 3 2 3 3 3 3\n",
       "        reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
       "        step_size_bar          (chain, draw) float64 64kB 0.8498 0.8498 ... 0.8669\n",
       "        lp                     (chain, draw) float64 64kB -16.29 -18.61 ... -11.36\n",
       "        perf_counter_diff      (chain, draw) float64 64kB 0.0003056 ... 0.0003297\n",
       "        perf_counter_start     (chain, draw) float64 64kB 37.75 37.75 ... 39.12\n",
       "    Attributes:\n",
       "        created_at:                 2026-04-25T12:14:28.293981+00:00\n",
       "        creation_library:           ArviZ\n",
       "        creation_library_version:   1.1.1dev0\n",
       "        creation_library_language:  Python\n",
       "        inference_library:          pymc\n",
       "        inference_library_version:  5.28.0+58.gf58491a3\n",
       "        sample_dims:                [&#x27;chain&#x27;, &#x27;draw&#x27;]\n",
       "        sampling_time:              5.952891111373901\n",
       "        tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div><div class='xr-obj-name'>&#x27;sample_stats&#x27;</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0f7daad7-90b6-4600-a13c-aabf0dfe897e' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-0f7daad7-90b6-4600-a13c-aabf0dfe897e' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div></li><li class='xr-section-item'><input id='section-406e26bb-1c2a-420e-9645-e813aa3de36f' class='xr-section-summary-in' type='checkbox' checked /><label for='section-406e26bb-1c2a-420e-9645-e813aa3de36f' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-8bb1a97a-4d5f-44ef-888c-19894146b2a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bb1a97a-4d5f-44ef-888c-19894146b2a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8ab8205-e3a6-4204-bbf3-4aacc273f75c' class='xr-var-data-in' type='checkbox'><label for='data-a8ab8205-e3a6-4204-bbf3-4aacc273f75c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-b87d24de-4d31-414b-a2f5-a15db05742be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b87d24de-4d31-414b-a2f5-a15db05742be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2f1f312-b7fb-467f-953d-14151047bed8' class='xr-var-data-in' type='checkbox'><label for='data-b2f1f312-b7fb-467f-953d-14151047bed8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999], shape=(2000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab3017f1-b783-498b-8163-16d723ea6e2b' class='xr-section-summary-in' type='checkbox' /><label for='section-ab3017f1-b783-498b-8163-16d723ea6e2b' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5117 0.8243 0.9935 ... 1.0 0.6885</div><input id='attrs-a202553f-c970-4f5c-b35a-aa1d7428504f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a202553f-c970-4f5c-b35a-aa1d7428504f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50bbe095-9842-4f90-a4bc-80c85df9326c' class='xr-var-data-in' type='checkbox'><label for='data-50bbe095-9842-4f90-a4bc-80c85df9326c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.51174226, 0.82433939, 0.99348419, ..., 0.49443367, 0.59231151,\n",
       "        0.88179185],\n",
       "       [1.        , 0.90708712, 0.93269626, ..., 0.82379309, 0.93972096,\n",
       "        0.91513955],\n",
       "       [0.63420517, 0.50988746, 0.81480311, ..., 0.71814159, 0.82955929,\n",
       "        1.        ],\n",
       "       [0.78426092, 1.        , 1.        , ..., 0.96975301, 1.        ,\n",
       "        0.68849767]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 7.0 7.0 7.0 ... 7.0 7.0 7.0 7.0</div><input id='attrs-7747c5d7-8932-4756-a7b6-cd3b50e42c05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7747c5d7-8932-4756-a7b6-cd3b50e42c05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86f2e663-fd3b-49ad-8bc8-aacc15126127' class='xr-var-data-in' type='checkbox'><label for='data-86f2e663-fd3b-49ad-8bc8-aacc15126127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3., 7., 7., ..., 3., 3., 7.],\n",
       "       [3., 3., 7., ..., 3., 7., 3.],\n",
       "       [7., 7., 3., ..., 3., 7., 3.],\n",
       "       [3., 7., 7., ..., 7., 7., 7.]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9907 0.5716 ... -0.4437 0.7114</div><input id='attrs-e419342c-503a-4032-a41c-cfc1ae8209fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e419342c-503a-4032-a41c-cfc1ae8209fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f2082a3-d5b5-4609-aaca-3414c638a470' class='xr-var-data-in' type='checkbox'><label for='data-4f2082a3-d5b5-4609-aaca-3414c638a470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.990742  ,  0.57160393, -0.58722209, ...,  1.33365222,\n",
       "         0.83333381, -0.89979905],\n",
       "       [-0.98864914,  0.15413002, -0.62117296, ...,  0.75209228,\n",
       "        -0.35072003, -0.48277515],\n",
       "       [ 1.00156507,  1.15092182,  0.76886967, ...,  0.60626821,\n",
       "         0.47703135, -0.50223453],\n",
       "       [ 0.50828547, -0.51555179, -0.28212408, ..., -0.29894456,\n",
       "        -0.44372241,  0.71136095]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergences</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-683a3880-e559-4100-9434-162df43bdfe9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-683a3880-e559-4100-9434-162df43bdfe9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f51dc843-9652-4b0a-90eb-bec260419c15' class='xr-var-data-in' type='checkbox'><label for='data-f51dc843-9652-4b0a-90eb-bec260419c15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-3 -4 -5 -3 3 3 2 ... 1 2 3 -4 6 4</div><input id='attrs-e93a9c32-ed59-4be7-b443-9f72c1726730' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e93a9c32-ed59-4be7-b443-9f72c1726730' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d788a778-23c0-45c6-84d4-2eea358b3b3f' class='xr-var-data-in' type='checkbox'><label for='data-d788a778-23c0-45c6-84d4-2eea358b3b3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3, -4, -5, ...,  0, -2, -2],\n",
       "       [-1,  2,  2, ...,  2,  2,  3],\n",
       "       [-4,  1,  2, ...,  3, -2, -1],\n",
       "       [ 1, -5,  4, ..., -4,  6,  4]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.85 22.25 23.79 ... 15.92 17.12</div><input id='attrs-ca03f03e-c90f-42a0-931a-4a7bac8a103a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca03f03e-c90f-42a0-931a-4a7bac8a103a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1961e38-42c9-4fa7-a423-cbbb6168dea7' class='xr-var-data-in' type='checkbox'><label for='data-c1961e38-42c9-4fa7-a423-cbbb6168dea7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[22.85499102, 22.253824  , 23.79159306, ..., 18.36514144,\n",
       "        17.83965557, 17.54161575],\n",
       "       [19.03095328, 19.11227085, 18.07097589, ..., 22.62602352,\n",
       "        19.74696462, 17.9359895 ],\n",
       "       [19.26840482, 20.35574472, 23.11597786, ..., 16.70460636,\n",
       "        20.66267519, 16.71046354],\n",
       "       [23.99288576, 20.18043294, 20.04696409, ..., 21.05442521,\n",
       "        15.92210829, 17.11867694]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-c1be3cdf-f207-40c4-b044-b9dc34245b48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1be3cdf-f207-40c4-b044-b9dc34245b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70f3b9b1-cea9-4a74-a307-b6d7067226f8' class='xr-var-data-in' type='checkbox'><label for='data-70f3b9b1-cea9-4a74-a307-b6d7067226f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-67d187c2-3479-46f7-9cf4-112828e92f18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67d187c2-3479-46f7-9cf4-112828e92f18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb553a38-ab37-4b5f-8568-e51dcaa2e88a' class='xr-var-data-in' type='checkbox'><label for='data-bb553a38-ab37-4b5f-8568-e51dcaa2e88a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3979 0.4932 ... -0.4437 -0.05516</div><input id='attrs-743fb4db-3ee1-45c1-96be-6170e586838c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-743fb4db-3ee1-45c1-96be-6170e586838c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f3e008-9852-4eaa-ae6e-b08c8e81c8cd' class='xr-var-data-in' type='checkbox'><label for='data-99f3e008-9852-4eaa-ae6e-b08c8e81c8cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.39792148,  0.49318996, -0.58722209, ...,  0.        ,\n",
       "         0.83333381, -0.7861699 ],\n",
       "       [-0.56053047,  0.15413002, -0.62117296, ..., -0.22828296,\n",
       "        -0.23838603,  0.28225004],\n",
       "       [-0.26734824,  0.80551207,  0.01931269, ...,  0.47273583,\n",
       "         0.19432755, -0.46040154],\n",
       "       [ 0.01873907, -0.19016432, -0.10244615, ..., -0.29894456,\n",
       "        -0.44372241, -0.05515555]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0003153 0.0006517 ... 0.0003378</div><input id='attrs-74746f79-7d8e-41db-ae17-aa22fcb743d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74746f79-7d8e-41db-ae17-aa22fcb743d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6700baa-f1a7-4b07-8603-cb82405823ce' class='xr-var-data-in' type='checkbox'><label for='data-f6700baa-f1a7-4b07-8603-cb82405823ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0003153 , 0.00065172, 0.00064331, ..., 0.00034069, 0.00034008,\n",
       "        0.00068047],\n",
       "       [0.00032311, 0.00032391, 0.00063884, ..., 0.00019314, 0.00034312,\n",
       "        0.00017632],\n",
       "       [0.00065287, 0.00060657, 0.0003152 , ..., 0.00021818, 0.00035878,\n",
       "        0.00021013],\n",
       "       [0.00032644, 0.00067573, 0.00067412, ..., 0.00033095, 0.00032562,\n",
       "        0.00033776]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8626 0.8626 ... 0.8859 0.8859</div><input id='attrs-d4cb51da-b1f4-4e0c-a1ea-f3c0eef52997' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4cb51da-b1f4-4e0c-a1ea-f3c0eef52997' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e613a6a-4cdb-45ca-92e8-53734831b804' class='xr-var-data-in' type='checkbox'><label for='data-4e613a6a-4cdb-45ca-92e8-53734831b804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.86259684, 0.86259684, 0.86259684, ..., 0.86259684, 0.86259684,\n",
       "        0.86259684],\n",
       "       [0.81031828, 0.81031828, 0.81031828, ..., 0.81031828, 0.81031828,\n",
       "        0.81031828],\n",
       "       [0.93527807, 0.93527807, 0.93527807, ..., 0.93527807, 0.93527807,\n",
       "        0.93527807],\n",
       "       [0.88589666, 0.88589666, 0.88589666, ..., 0.88589666, 0.88589666,\n",
       "        0.88589666]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-cb436fe1-c679-41ca-a348-a361c7b258fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb436fe1-c679-41ca-a348-a361c7b258fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e559ca04-1ea6-41d4-b45f-bee64e8d1e4a' class='xr-var-data-in' type='checkbox'><label for='data-e559ca04-1ea6-41d4-b45f-bee64e8d1e4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 3 3 3 3 2 3 ... 3 3 3 2 3 3 3 3</div><input id='attrs-a32aa5da-531e-43cd-bc66-1ea8bf3a83db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a32aa5da-531e-43cd-bc66-1ea8bf3a83db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dd3db79-9136-4691-b431-78f78ba043d6' class='xr-var-data-in' type='checkbox'><label for='data-5dd3db79-9136-4691-b431-78f78ba043d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 3, 3, ..., 2, 2, 3],\n",
       "       [2, 2, 3, ..., 2, 3, 2],\n",
       "       [3, 3, 2, ..., 2, 3, 2],\n",
       "       [2, 3, 3, ..., 3, 3, 3]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-257b69cc-860a-400c-828e-ef123b8588ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-257b69cc-860a-400c-828e-ef123b8588ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f69d10d6-c245-4d5c-a4cc-c54e242b1bce' class='xr-var-data-in' type='checkbox'><label for='data-f69d10d6-c245-4d5c-a4cc-c54e242b1bce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8498 0.8498 ... 0.8669 0.8669</div><input id='attrs-4f16b898-97ed-4440-bf45-cd2a3d7a4be8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f16b898-97ed-4440-bf45-cd2a3d7a4be8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0448a9f8-f08a-4a40-a4c5-af6faec25168' class='xr-var-data-in' type='checkbox'><label for='data-0448a9f8-f08a-4a40-a4c5-af6faec25168' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.84980907, 0.84980907, 0.84980907, ..., 0.84980907, 0.84980907,\n",
       "        0.84980907],\n",
       "       [0.85807006, 0.85807006, 0.85807006, ..., 0.85807006, 0.85807006,\n",
       "        0.85807006],\n",
       "       [0.86529605, 0.86529605, 0.86529605, ..., 0.86529605, 0.86529605,\n",
       "        0.86529605],\n",
       "       [0.86693771, 0.86693771, 0.86693771, ..., 0.86693771, 0.86693771,\n",
       "        0.86693771]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.29 -18.61 ... -11.68 -11.36</div><input id='attrs-1703781a-e677-457f-b5be-4ef3ff97323d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1703781a-e677-457f-b5be-4ef3ff97323d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9009fc86-4176-4ffc-b293-8025f2a62524' class='xr-var-data-in' type='checkbox'><label for='data-9009fc86-4176-4ffc-b293-8025f2a62524' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-16.29004717, -18.60854869, -15.15277225, ..., -11.00227046,\n",
       "        -15.72383988, -11.20869783],\n",
       "       [-14.47616559, -15.12581827, -12.19859569, ..., -15.36282214,\n",
       "        -14.21024487, -15.8718359 ],\n",
       "       [-11.86282826, -16.32912968, -16.24914376, ..., -14.60302444,\n",
       "        -15.2715556 , -12.77974001],\n",
       "       [-17.2816567 , -16.12980302, -15.49714683, ..., -14.54270332,\n",
       "        -11.68068426, -11.36290827]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0003056 0.0006314 ... 0.0003297</div><input id='attrs-ea4d9f5a-c12f-4a67-8895-820fadb696bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea4d9f5a-c12f-4a67-8895-820fadb696bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6042dae-30e8-4bc3-ae5a-bfa0692853be' class='xr-var-data-in' type='checkbox'><label for='data-b6042dae-30e8-4bc3-ae5a-bfa0692853be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00030564, 0.00063144, 0.00062311, ..., 0.00033143, 0.00033087,\n",
       "        0.00066195],\n",
       "       [0.00031316, 0.00031381, 0.00061899, ..., 0.00018785, 0.00033379,\n",
       "        0.00017152],\n",
       "       [0.00063268, 0.00058776, 0.00030542, ..., 0.0002126 , 0.00034996,\n",
       "        0.00020476],\n",
       "       [0.00031625, 0.0006547 , 0.00065333, ..., 0.00032304, 0.00031783,\n",
       "        0.00032966]], shape=(4, 2000))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>37.75 37.75 37.75 ... 39.12 39.12</div><input id='attrs-6f50a012-0119-479e-8ae5-cfe82d2e35a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f50a012-0119-479e-8ae5-cfe82d2e35a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf5d8db3-57ba-4102-85bc-44163fce9a20' class='xr-var-data-in' type='checkbox'><label for='data-cf5d8db3-57ba-4102-85bc-44163fce9a20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[37.75302424, 37.7534525 , 37.75420829, ..., 38.76399762,\n",
       "        38.76446004, 38.76492932],\n",
       "       [38.03858025, 38.03902416, 38.03946503, ..., 39.07884391,\n",
       "        39.07910813, 39.07951781],\n",
       "       [37.96663349, 37.96739167, 37.96811278, ..., 39.2227741 ,\n",
       "        39.22308952, 39.22351981],\n",
       "       [37.71619748, 37.71668577, 37.71747385, ..., 39.11997091,\n",
       "        39.1203649 , 39.12075404]], shape=(4, 2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f43d1681-001f-4950-9be7-e223520f5979' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f43d1681-001f-4950-9be7-e223520f5979' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2026-04-25T12:14:28.293981+00:00</dd><dt><span>creation_library :</span></dt><dd>ArviZ</dd><dt><span>creation_library_version :</span></dt><dd>1.1.1dev0</dd><dt><span>creation_library_language :</span></dt><dd>Python</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.28.0+58.gf58491a3</dd><dt><span>sample_dims :</span></dt><dd>[&#x27;chain&#x27;, &#x27;draw&#x27;]</dd><dt><span>sampling_time :</span></dt><dd>5.952891111373901</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataTree 'sample_stats'>\n",
       "Group: /sample_stats\n",
       "    Dimensions:                (chain: 4, draw: 2000)\n",
       "    Coordinates:\n",
       "      * chain                  (chain) int64 32B 0 1 2 3\n",
       "      * draw                   (draw) int64 16kB 0 1 2 3 4 ... 1996 1997 1998 1999\n",
       "    Data variables: (12/18)\n",
       "        acceptance_rate        (chain, draw) float64 64kB 0.5117 0.8243 ... 0.6885\n",
       "        n_steps                (chain, draw) float64 64kB 3.0 7.0 7.0 ... 7.0 7.0\n",
       "        max_energy_error       (chain, draw) float64 64kB 0.9907 0.5716 ... 0.7114\n",
       "        divergences            (chain, draw) int64 64kB 0 0 0 0 0 0 ... 0 0 0 0 0 0\n",
       "        index_in_trajectory    (chain, draw) int64 64kB -3 -4 -5 -3 3 ... 2 3 -4 6 4\n",
       "        energy                 (chain, draw) float64 64kB 22.85 22.25 ... 17.12\n",
       "        ...                     ...\n",
       "        tree_depth             (chain, draw) int64 64kB 2 3 3 3 3 3 ... 3 2 3 3 3 3\n",
       "        reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
       "        step_size_bar          (chain, draw) float64 64kB 0.8498 0.8498 ... 0.8669\n",
       "        lp                     (chain, draw) float64 64kB -16.29 -18.61 ... -11.36\n",
       "        perf_counter_diff      (chain, draw) float64 64kB 0.0003056 ... 0.0003297\n",
       "        perf_counter_start     (chain, draw) float64 64kB 37.75 37.75 ... 39.12\n",
       "    Attributes:\n",
       "        created_at:                 2026-04-25T12:14:28.293981+00:00\n",
       "        creation_library:           ArviZ\n",
       "        creation_library_version:   1.1.1dev0\n",
       "        creation_library_language:  Python\n",
       "        inference_library:          pymc\n",
       "        inference_library_version:  5.28.0+58.gf58491a3\n",
       "        sample_dims:                ['chain', 'draw']\n",
       "        sampling_time:              5.952891111373901\n",
       "        tuning_steps:               1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata.sample_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample statistics variables are defined as follows:\n",
    "\n",
    "- `process_time_diff`: The time it took to draw the sample, as defined by the python standard library time.process_time. This counts all the CPU time, including worker processes in BLAS and OpenMP.\n",
    "\n",
    "- `step_size`: The current integration step size.\n",
    "\n",
    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
    "\n",
    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
    "\n",
    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
    "\n",
    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
    "\n",
    "- `perf_counter_diff`: The time it took to draw the sample, as defined by the python standard library time.perf_counter (wall time).\n",
    "\n",
    "- `perf_counter_start`: The value of time.perf_counter at the beginning of the computation of the draw.\n",
    "\n",
    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
    "\n",
    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
    "\n",
    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
    "\n",
    "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
    "\n",
    "- `tree_depth`: The number of tree doublings in the balanced binary tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some points to `Note`:\n",
    "- Some of the sample statistics used by NUTS are renamed when converting to `InferenceData` to follow {ref}`ArviZ's naming convention <arviz:schema>`, while some are specific to PyMC3 and keep their internal PyMC3 name in the resulting InferenceData object.\n",
    "- `InferenceData` also stores additional info like the date, versions used, sampling time and tuning steps as attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idata.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x535.887 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_dist(\n",
    "    idata,\n",
    "    group=\"sample_stats\",\n",
    "    var_names=\"acceptance_rate\",\n",
    "    kind=\"hist\",\n",
    "    visuals={\"credible_interval\": False},\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check if there are any divergences, if yes, how many?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "  margin-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-group-box-contents > label {\n",
       "  color: var(--xr-font-color2);\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item > input,\n",
       ".xr-group-box-contents > input,\n",
       ".xr-array-wrap > input {\n",
       "  display: block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item > input + label,\n",
       ".xr-var-item > input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label,\n",
       ".xr-var-item > input:enabled + label,\n",
       ".xr-array-wrap > input:enabled + label,\n",
       ".xr-group-box-contents > input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:focus-visible + label,\n",
       ".xr-var-item > input:focus-visible + label,\n",
       ".xr-array-wrap > input:focus-visible + label,\n",
       ".xr-group-box-contents > input:focus-visible + label {\n",
       "  outline: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label:hover,\n",
       ".xr-var-item > input:enabled + label:hover,\n",
       ".xr-array-wrap > input:enabled + label:hover,\n",
       ".xr-group-box-contents > input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "  white-space: nowrap;\n",
       "}\n",
       "\n",
       ".xr-section-summary > em {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       ".xr-span-grid {\n",
       "  grid-column-end: -1;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.6em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details,\n",
       ".xr-group-box-contents > label {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in ~ .xr-section-details {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-children {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 100%;\n",
       "  grid-column: 1 / -1;\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 30px auto;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 26px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > label::before {\n",
       "  content: \"📂\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label::before {\n",
       "  content: \"📁\";\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label {\n",
       "  padding-bottom: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-ellipsis {\n",
       "  font-size: 1.4em;\n",
       "  font-weight: 900;\n",
       "  color: var(--xr-font-color2);\n",
       "  letter-spacing: 0.15em;\n",
       "  cursor: default;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;diverging&#x27; ()&gt; Size: 8B\n",
       "array(0)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;diverging&#x27;</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ff55eeb7-ea0f-481a-b8b8-61cd24b116bc' class='xr-array-in' type='checkbox' checked><label for='section-ff55eeb7-ea0f-481a-b8b8-61cd24b116bc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'diverging' ()> Size: 8B\n",
       "array(0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata.sample_stats[\"diverging\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case no divergences are found. If there are any, check [this notebook](https://github.com/pymc-devs/pymc-examples/blob/main/examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb) for  information on handling divergences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often useful to compare the overall distribution of the\n",
    "energy levels with the change of energy between successive samples.\n",
    "Ideally, they should be very similar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2400x535.887 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_energy(idata);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple samplers\n",
    "\n",
    "If multiple samplers are used for the same model (e.g. for continuous and discrete variables), the exported values are merged or stacked along a new axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"], \"obs\": [\"mu1\"]}\n",
    "dims = {\"accept\": [\"step\"]}\n",
    "\n",
    "with pm.Model(coords=coords) as model:\n",
    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, dims=\"obs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">BinaryMetropolis: [mu1]\n",
      ">Metropolis: [mu2]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f22602df6fae4aebbe0dfe44ea83070d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 17 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    step1 = pm.BinaryMetropolis([mu1])\n",
    "    step2 = pm.Metropolis([mu2])\n",
    "    idata = pm.sample(\n",
    "        10000,\n",
    "        init=None,\n",
    "        step=[step1, step2],\n",
    "        chains=4,\n",
    "        tune=1000,\n",
    "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accepted', 'accept', 'scaling', 'p_jump']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(idata.sample_stats.data_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both samplers export `accept`, so we get one acceptance probability for each sampler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x535.887 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_dist(\n",
    "    idata,\n",
    "    group=\"sample_stats\",\n",
    "    var_names=\"accept\",\n",
    "    kind=\"ecdf\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that `accept` sometimes takes really high values (jumps from regions of low probability to regions of much higher probability)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "  margin-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-group-box-contents > label {\n",
       "  color: var(--xr-font-color2);\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item > input,\n",
       ".xr-group-box-contents > input,\n",
       ".xr-array-wrap > input {\n",
       "  display: block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item > input + label,\n",
       ".xr-var-item > input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label,\n",
       ".xr-var-item > input:enabled + label,\n",
       ".xr-array-wrap > input:enabled + label,\n",
       ".xr-group-box-contents > input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:focus-visible + label,\n",
       ".xr-var-item > input:focus-visible + label,\n",
       ".xr-array-wrap > input:focus-visible + label,\n",
       ".xr-group-box-contents > input:focus-visible + label {\n",
       "  outline: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label:hover,\n",
       ".xr-var-item > input:enabled + label:hover,\n",
       ".xr-array-wrap > input:enabled + label:hover,\n",
       ".xr-group-box-contents > input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "  white-space: nowrap;\n",
       "}\n",
       "\n",
       ".xr-section-summary > em {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       ".xr-span-grid {\n",
       "  grid-column-end: -1;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.6em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details,\n",
       ".xr-group-box-contents > label {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in ~ .xr-section-details {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-children {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 100%;\n",
       "  grid-column: 1 / -1;\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 30px auto;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 26px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > label::before {\n",
       "  content: \"📂\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label::before {\n",
       "  content: \"📁\";\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label {\n",
       "  padding-bottom: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-ellipsis {\n",
       "  font-size: 1.4em;\n",
       "  font-weight: 900;\n",
       "  color: var(--xr-font-color2);\n",
       "  letter-spacing: 0.15em;\n",
       "  cursor: default;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 4, accept_dim_0: 2)&gt; Size: 64B\n",
       "array([[  3.75      , 153.17870672],\n",
       "       [  3.75      , 734.08661568],\n",
       "       [  3.75      , 127.786444  ],\n",
       "       [  3.75      , 252.56104158]])\n",
       "Coordinates:\n",
       "  * chain         (chain) int64 32B 0 1 2 3\n",
       "  * accept_dim_0  (accept_dim_0) int64 16B 0 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;accept&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>accept_dim_0</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-359a338d-caf2-4ec9-aa40-6c3932f692c8' class='xr-array-in' type='checkbox' checked><label for='section-359a338d-caf2-4ec9-aa40-6c3932f692c8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.75 153.2 3.75 734.1 3.75 127.8 3.75 252.6</span></div><div class='xr-array-data'><pre>array([[  3.75      , 153.17870672],\n",
       "       [  3.75      , 734.08661568],\n",
       "       [  3.75      , 127.786444  ],\n",
       "       [  3.75      , 252.56104158]])</pre></div></div></li><li class='xr-section-item'><input id='section-cfb4ec50-8f76-4932-bce8-9c58591a4dab' class='xr-section-summary-in' type='checkbox' checked /><label for='section-cfb4ec50-8f76-4932-bce8-9c58591a4dab' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-1e0562cb-044e-4c42-93be-6b390600fd6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e0562cb-044e-4c42-93be-6b390600fd6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cf0ce8c-4fff-433d-9301-45eff2c17414' class='xr-var-data-in' type='checkbox'><label for='data-8cf0ce8c-4fff-433d-9301-45eff2c17414' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>accept_dim_0</span></div><div class='xr-var-dims'>(accept_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-416ef35f-8ac0-4862-b189-e69d4d2580a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-416ef35f-8ac0-4862-b189-e69d4d2580a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47cfb0b8-6ac2-4530-9bb2-2b1e370c2130' class='xr-var-data-in' type='checkbox'><label for='data-47cfb0b8-6ac2-4530-9bb2-2b1e370c2130' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li></ul></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'accept' (chain: 4, accept_dim_0: 2)> Size: 64B\n",
       "array([[  3.75      , 153.17870672],\n",
       "       [  3.75      , 734.08661568],\n",
       "       [  3.75      , 127.786444  ],\n",
       "       [  3.75      , 252.56104158]])\n",
       "Coordinates:\n",
       "  * chain         (chain) int64 32B 0 1 2 3\n",
       "  * accept_dim_0  (accept_dim_0) int64 16B 0 1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Range of accept values\n",
    "idata.sample_stats[\"accept\"].max(\"draw\") - idata.sample_stats[\"accept\"].min(\"draw\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "* Updated by Meenal Jhajharia in April 2021 ([pymc-examples#95](https://github.com/pymc-devs/pymc-examples/pull/95))\n",
    "* Updated to v4 by Christian Luhmann in May 2022 ([pymc-examples#338](https://github.com/pymc-devs/pymc-examples/pull/338))\n",
    "* Updated by Osvaldo Martin in Dec 2025\n",
    "* Updated by Osvaldo Martin in Apr 2026"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat, 25 Apr 2026\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.14.4\n",
      "IPython version      : 9.12.0\n",
      "\n",
      "arviz     : 1.1.0\n",
      "matplotlib: 3.10.8\n",
      "pymc      : 5.28.0+58.gf58491a3\n",
      "\n",
      "Watermark: 2.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
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  "jupytext": {
   "notebook_metadata_filter": "substitutions"
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   "display_name": "arviz_1",
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   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
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