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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 v5.27.0\n"
     ]
    }
   ],
   "source": [
    "import arviz.preview 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": "6de69b91bfbe497e8891c4df49329c7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
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    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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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 4 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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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1MB\n",
       "Dimensions:                (chain: 4, draw: 2000)\n",
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       "  * chain                  (chain) int64 32B 0 1 2 3\n",
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       "    ...                     ...\n",
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       "    inference_library_version:  5.27.0\n",
       "    sampling_time:              3.9331319332122803\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f8fdeff4-a86e-4d96-9da3-c526fb74125f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8fdeff4-a86e-4d96-9da3-c526fb74125f' class='xr-section-summary'  title='Expand/collapse section'>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><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fa42d267-9124-4896-b61f-78f69d1fd70d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa42d267-9124-4896-b61f-78f69d1fd70d' class='xr-section-summary' >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-a27baba7-3cda-4e37-bad2-d9a4aefa90a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a27baba7-3cda-4e37-bad2-d9a4aefa90a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-802459c4-9807-4b4c-ab4f-d2a170e2fcb5' class='xr-var-data-in' type='checkbox'><label for='data-802459c4-9807-4b4c-ab4f-d2a170e2fcb5' 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-8289d34c-ccf8-4454-bc89-c54a33d9bdf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8289d34c-ccf8-4454-bc89-c54a33d9bdf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83737513-7c72-45b4-a184-2669f8b23752' class='xr-var-data-in' type='checkbox'><label for='data-83737513-7c72-45b4-a184-2669f8b23752' 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-4b7cd1ba-2c62-4502-b173-06918ae3cb6a' class='xr-section-summary-in' type='checkbox'  ><label for='section-4b7cd1ba-2c62-4502-b173-06918ae3cb6a' class='xr-section-summary' >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>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-70a6e040-2248-4fc4-9591-42fb31caf59f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70a6e040-2248-4fc4-9591-42fb31caf59f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-354d9002-6cae-4ab6-9716-22d792cf1fcd' class='xr-var-data-in' type='checkbox'><label for='data-354d9002-6cae-4ab6-9716-22d792cf1fcd' 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'>-2 -6 2 3 3 2 ... -4 -6 -3 1 -3 0</div><input id='attrs-cbdc7f58-dde7-4b3e-ae5d-a4a4eefb6579' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbdc7f58-dde7-4b3e-ae5d-a4a4eefb6579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48c8be0-e4bf-4bc4-86ca-3a4bfeca5ff2' class='xr-var-data-in' type='checkbox'><label for='data-b48c8be0-e4bf-4bc4-86ca-3a4bfeca5ff2' 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, -6,  2, ..., -2, -1,  3],\n",
       "       [-5, -3,  0, ...,  1, -2,  6],\n",
       "       [ 3,  1,  4, ..., -3,  1, -4],\n",
       "       [-4, -4, -3, ...,  1, -3,  0]], 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'>0.0002337 0.0004433 ... 0.0001852</div><input id='attrs-0db0f627-6cf0-4f6b-8be7-f68b607c11a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0db0f627-6cf0-4f6b-8be7-f68b607c11a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87125783-4d69-47ad-b68d-e0047eaaab19' class='xr-var-data-in' type='checkbox'><label for='data-87125783-4d69-47ad-b68d-e0047eaaab19' 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([[0.00023368, 0.00044332, 0.00025602, ..., 0.00063735, 0.00042918,\n",
       "        0.0004326 ],\n",
       "       [0.00035478, 0.00034463, 0.00021375, ..., 0.00020731, 0.0003798 ,\n",
       "        0.00046447],\n",
       "       [0.00074387, 0.00046456, 0.00039677, ..., 0.00035469, 0.00018985,\n",
       "        0.00039308],\n",
       "       [0.00048815, 0.00045325, 0.00048575, ..., 0.00016354, 0.00032395,\n",
       "        0.00018525]], 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'>17.5 15.6 19.92 ... 15.72 21.14</div><input id='attrs-ec4d5511-72ac-4fd9-8d88-5b42c7b94768' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec4d5511-72ac-4fd9-8d88-5b42c7b94768' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76a7d560-82ee-4186-b767-0bac4f2a39cb' class='xr-var-data-in' type='checkbox'><label for='data-76a7d560-82ee-4186-b767-0bac4f2a39cb' 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([[17.49572566, 15.60347944, 19.9203982 , ..., 15.85848568,\n",
       "        17.91361846, 18.19975923],\n",
       "       [16.86820145, 18.16274847, 17.75581803, ..., 17.88621367,\n",
       "        19.34619931, 21.61252149],\n",
       "       [16.18358382, 19.5891873 , 18.7648976 , ..., 18.54819117,\n",
       "        20.98086119, 19.33772806],\n",
       "       [19.94466808, 19.61880787, 21.96548329, ..., 18.2593644 ,\n",
       "        15.72440748, 21.13523192]], 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'>0.6809 -0.3205 ... 0.4838 1.576</div><input id='attrs-cd74575e-f1fb-46f1-8a2a-89fcfd1a64f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd74575e-f1fb-46f1-8a2a-89fcfd1a64f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-434bd183-9f39-4173-b5b7-44761fd55722' class='xr-var-data-in' type='checkbox'><label for='data-434bd183-9f39-4173-b5b7-44761fd55722' 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([[ 0.68088044, -0.32050096,  0.84460163, ...,  0.40584118,\n",
       "         0.66382456,  0.80183057],\n",
       "       [ 0.22111484,  0.45387973,  0.44204538, ...,  0.7900379 ,\n",
       "         0.77361688, -0.32036278],\n",
       "       [ 0.33383007,  0.38035051,  0.49543638, ...,  0.92213578,\n",
       "         1.25230057,  0.38176965],\n",
       "       [ 0.1871094 , -0.65745866,  0.36171947, ..., -0.54459322,\n",
       "         0.48377679,  1.57552933]], 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'>0.8225 0.8225 ... 0.8225 0.8225</div><input id='attrs-562eb32b-c882-494a-b003-563537cb9bbf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-562eb32b-c882-494a-b003-563537cb9bbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccf85edc-d94c-4502-9a05-d11f26258e0e' class='xr-var-data-in' type='checkbox'><label for='data-ccf85edc-d94c-4502-9a05-d11f26258e0e' 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([[0.82247367, 0.82247367, 0.82247367, ..., 0.82247367, 0.82247367,\n",
       "        0.82247367],\n",
       "       [0.84974896, 0.84974896, 0.84974896, ..., 0.84974896, 0.84974896,\n",
       "        0.84974896],\n",
       "       [0.85126194, 0.85126194, 0.85126194, ..., 0.85126194, 0.85126194,\n",
       "        0.85126194],\n",
       "       [0.8225178 , 0.8225178 , 0.8225178 , ..., 0.8225178 , 0.8225178 ,\n",
       "        0.8225178 ]], 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'>-13.74 -12.26 ... -11.83 -11.83</div><input id='attrs-7b3809d9-3677-41ce-916c-a1b5314d6d76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b3809d9-3677-41ce-916c-a1b5314d6d76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ef7e632-2e57-4f61-82f4-d57f976424d5' class='xr-var-data-in' type='checkbox'><label for='data-9ef7e632-2e57-4f61-82f4-d57f976424d5' 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([[-13.73812207, -12.25543176, -17.40721333, ..., -12.79197709,\n",
       "        -13.22825199, -12.53317939],\n",
       "       [-13.47400274, -12.48019479, -12.48019479, ..., -12.31105702,\n",
       "        -16.43188489, -14.43830315],\n",
       "       [-14.19603167, -12.98188858, -13.78267394, ..., -12.12644274,\n",
       "        -13.95622965, -14.02107485],\n",
       "       [-16.12489338, -15.35613182, -15.53634553, ..., -11.52357352,\n",
       "        -11.8272748 , -11.8272748 ]], 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 3.0 7.0 ... 7.0 3.0 7.0 3.0</div><input id='attrs-2c24fa4c-b8e3-4a1e-aaa1-cb3dd49ff205' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c24fa4c-b8e3-4a1e-aaa1-cb3dd49ff205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10719216-9b2e-4d3c-970f-fe5c75dacb07' class='xr-var-data-in' type='checkbox'><label for='data-10719216-9b2e-4d3c-970f-fe5c75dacb07' 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., 3., ..., 7., 7., 7.],\n",
       "       [7., 7., 3., ..., 3., 3., 7.],\n",
       "       [7., 7., 7., ..., 7., 3., 7.],\n",
       "       [7., 7., 7., ..., 3., 7., 3.]], 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'>0.01178 -0.1823 ... 0.01214 0.0</div><input id='attrs-32eee522-f4a9-4ab9-bf1a-a39d119c0300' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32eee522-f4a9-4ab9-bf1a-a39d119c0300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-307c956c-a599-4896-b1de-48be0da7b327' class='xr-var-data-in' type='checkbox'><label for='data-307c956c-a599-4896-b1de-48be0da7b327' 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([[ 0.01178261, -0.1823171 ,  0.84460163, ...,  0.19823157,\n",
       "        -0.09836398, -0.07502961],\n",
       "       [ 0.08325062, -0.14781848,  0.        , ...,  0.0942182 ,\n",
       "         0.77361688, -0.32036278],\n",
       "       [ 0.33383007, -0.20736462,  0.17783116, ...,  0.04008211,\n",
       "         0.42046335,  0.00792548],\n",
       "       [ 0.1871094 , -0.17636257, -0.02691993, ..., -0.54459322,\n",
       "         0.01213823,  0.        ]], 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'>0.0002335 0.0004432 ... 0.000185</div><input id='attrs-15ead2f0-5537-48eb-be75-83faefd06257' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15ead2f0-5537-48eb-be75-83faefd06257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4251f2b-5674-426b-8ee1-8a0539896be2' class='xr-var-data-in' type='checkbox'><label for='data-b4251f2b-5674-426b-8ee1-8a0539896be2' 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([[0.00023354, 0.0004432 , 0.00025587, ..., 0.00063714, 0.00042901,\n",
       "        0.00117643],\n",
       "       [0.0003548 , 0.00034456, 0.00021358, ..., 0.00020715, 0.00038003,\n",
       "        0.00046441],\n",
       "       [0.00150678, 0.00046469, 0.00039683, ..., 0.00035422, 0.00018962,\n",
       "        0.00039277],\n",
       "       [0.00048816, 0.00045304, 0.00048556, ..., 0.0001634 , 0.00032334,\n",
       "        0.00018497]], 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-4a6139b9-2a93-4d3a-b8b6-467659f90ea6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a6139b9-2a93-4d3a-b8b6-467659f90ea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34c904d1-74a7-43d6-a725-5e6d98c54633' class='xr-var-data-in' type='checkbox'><label for='data-34c904d1-74a7-43d6-a725-5e6d98c54633' 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 2 3 2 3 3 3 ... 3 3 3 3 3 2 3 2</div><input id='attrs-5568ebca-5215-40e7-a79d-db565d7e9709' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5568ebca-5215-40e7-a79d-db565d7e9709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12a78937-1c87-42ce-8928-55759182d661' class='xr-var-data-in' type='checkbox'><label for='data-12a78937-1c87-42ce-8928-55759182d661' 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, 2, ..., 3, 3, 3],\n",
       "       [3, 3, 2, ..., 2, 2, 3],\n",
       "       [3, 3, 3, ..., 3, 2, 3],\n",
       "       [3, 3, 3, ..., 2, 3, 2]], 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-19c9053f-85d9-413e-a6fe-ed3999de23c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19c9053f-85d9-413e-a6fe-ed3999de23c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91d43769-ded2-4208-9deb-1549a78c119a' class='xr-var-data-in' type='checkbox'><label for='data-91d43769-ded2-4208-9deb-1549a78c119a' 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>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'>0.8315 1.0 0.5935 ... 0.8112 0.3206</div><input id='attrs-2440baf5-c6a7-47b2-af08-9cc9382106ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2440baf5-c6a7-47b2-af08-9cc9382106ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34e5f0fc-7b55-41cb-8216-9afb8f9df993' class='xr-var-data-in' type='checkbox'><label for='data-34e5f0fc-7b55-41cb-8216-9afb8f9df993' 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([[0.83148589, 1.        , 0.5934892 , ..., 0.86850182, 0.74399129,\n",
       "        0.78076157],\n",
       "       [0.91551905, 0.84346907, 0.72401941, ..., 0.62237876, 0.55351878,\n",
       "        0.91977923],\n",
       "       [0.87065556, 0.85257561, 0.79700361, ..., 0.61398443, 0.43140717,\n",
       "        0.80443692],\n",
       "       [0.92090391, 0.97630045, 0.87795146, ..., 0.89827457, 0.81115816,\n",
       "        0.32064497]], 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-d760e055-93b9-4830-af90-e8464002e909' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d760e055-93b9-4830-af90-e8464002e909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa450a3-a901-4244-b114-16067edb4488' class='xr-var-data-in' type='checkbox'><label for='data-daa450a3-a901-4244-b114-16067edb4488' 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>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'>0.8814 0.8814 ... 0.8207 0.8207</div><input id='attrs-be13b205-ed85-4326-9148-afc52b532d00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be13b205-ed85-4326-9148-afc52b532d00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d53ef83-be8f-4345-8058-28a1943eacdf' class='xr-var-data-in' type='checkbox'><label for='data-8d53ef83-be8f-4345-8058-28a1943eacdf' 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([[0.88137396, 0.88137396, 0.88137396, ..., 0.88137396, 0.88137396,\n",
       "        0.88137396],\n",
       "       [0.85453782, 0.85453782, 0.85453782, ..., 0.85453782, 0.85453782,\n",
       "        0.85453782],\n",
       "       [0.86269247, 0.86269247, 0.86269247, ..., 0.86269247, 0.86269247,\n",
       "        0.86269247],\n",
       "       [0.82066284, 0.82066284, 0.82066284, ..., 0.82066284, 0.82066284,\n",
       "        0.82066284]], 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-2a613113-ccc0-4c42-b6e9-08935e9fb6f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a613113-ccc0-4c42-b6e9-08935e9fb6f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44790a43-93ea-4e49-b2de-46d29c0f7db7' class='xr-var-data-in' type='checkbox'><label for='data-44790a43-93ea-4e49-b2de-46d29c0f7db7' 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>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'>6.756e+03 6.756e+03 ... 6.758e+03</div><input id='attrs-5d670508-9843-4d6e-adcd-f6328b25bfa3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d670508-9843-4d6e-adcd-f6328b25bfa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d94cf80-32a3-4122-ac27-de758fe883e9' class='xr-var-data-in' type='checkbox'><label for='data-3d94cf80-32a3-4122-ac27-de758fe883e9' 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([[6755.50105589, 6755.50139737, 6755.50260371, ..., 6757.70397927,\n",
       "        6757.70473343, 6757.70583702],\n",
       "       [6755.6981072 , 6755.69855574, 6755.69899207, ..., 6757.89341022,\n",
       "        6757.89965191, 6757.90017506],\n",
       "       [6755.96307143, 6755.97048842, 6755.97111305, ..., 6758.06007931,\n",
       "        6758.06052395, 6758.06080753],\n",
       "       [6756.20182894, 6756.20242818, 6756.20386539, ..., 6758.25976638,\n",
       "        6758.25998932, 6758.26039712]], shape=(4, 2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f6958ef-b975-43fc-abe4-38b3df64c226' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f6958ef-b975-43fc-abe4-38b3df64c226' class='xr-section-summary' >Indexes: <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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8763817d-a60a-46ee-9d66-d4241c1b7a83' class='xr-index-data-in' type='checkbox'/><label for='index-8763817d-a60a-46ee-9d66-d4241c1b7a83' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a58de4c0-081c-4a10-8932-13c6a27a060c' class='xr-index-data-in' type='checkbox'/><label for='index-a58de4c0-081c-4a10-8932-13c6a27a060c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48d58cfb-31b6-4628-8ea4-76f5e0a9852a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48d58cfb-31b6-4628-8ea4-76f5e0a9852a' class='xr-section-summary' >Attributes: <span>(6)</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>2025-12-31T10:07:15.224484+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.27.0</dd><dt><span>sampling_time :</span></dt><dd>3.9331319332122803</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 1MB\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",
       "    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 -2 -6 2 3 3 ... -6 -3 1 -3 0\n",
       "    process_time_diff      (chain, draw) float64 64kB 0.0002337 ... 0.0001852\n",
       "    energy                 (chain, draw) float64 64kB 17.5 15.6 ... 15.72 21.14\n",
       "    max_energy_error       (chain, draw) float64 64kB 0.6809 -0.3205 ... 1.576\n",
       "    step_size_bar          (chain, draw) float64 64kB 0.8225 0.8225 ... 0.8225\n",
       "    ...                     ...\n",
       "    diverging              (chain, draw) bool 8kB False False ... False False\n",
       "    acceptance_rate        (chain, draw) float64 64kB 0.8315 1.0 ... 0.3206\n",
       "    largest_eigval         (chain, draw) float64 64kB nan nan nan ... nan nan\n",
       "    step_size              (chain, draw) float64 64kB 0.8814 0.8814 ... 0.8207\n",
       "    reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
       "    perf_counter_start     (chain, draw) float64 64kB 6.756e+03 ... 6.758e+03\n",
       "Attributes:\n",
       "    created_at:                 2025-12-31T10:07:15.224484+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.27.0\n",
       "    sampling_time:              3.9331319332122803\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": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "</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-array-name'>'diverging'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6e4433a3-c3ed-4a1b-8fdd-3027e1947c6e' class='xr-array-in' type='checkbox' checked><label for='section-6e4433a3-c3ed-4a1b-8fdd-3027e1947c6e' 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><li class='xr-section-item'><input id='section-f625e136-2642-4dd2-b61e-f549c9fe6294' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f625e136-2642-4dd2-b61e-f549c9fe6294' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-51f4a54c-58a4-425e-b380-d3df61075339' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51f4a54c-58a4-425e-b380-d3df61075339' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0f3b6610-d68e-48f7-b72c-8871561acb91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0f3b6610-d68e-48f7-b72c-8871561acb91' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></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": [
    {
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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": "09152388238143d7a19003fbc6e6d34c",
       "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 5 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', 'scaling', 'accept', '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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zsrK0fv36Ass79sV7773XVPscy9EX3cOb9vv27dsNgZROnTqZuiG9TZs2qlOnjj0dExOjM2fOmGof3MsT56qEhATt3r3bnq5bt67hieOChIeHq2PHjvb0uXPntGPHDlPtK++sVqvq1atnyCvob8RT27eoxxTHvvLTTz8VWHb9+vWG97v37t27wCefc+vZs6fh2tsff/xR6JPy3sCVfuIpnHuQGwF2AABQZGXxxy5QlsXHxxvS1atXL6WWAGXH22+/rcTERElXnwC46667SrlFAMzYsGGDIe347tCC1KxZ0zD17pEjR/KcH+Edtm/fbriZsGXLlqbfb+rYnxz7m7PPzPbFVq1aGdoTHR2trKwsU8uiYN603zdu3GhIt2vXzlQdjmVtNluedcEzPHGuKk4/caXPexvHV67lDjzn5ontm5WVpejoaHu6QoUKTqfjdlaHs791x8/M9ker1WpoT0pKinbu3Glq2fLObD/xFM49yI0AOwAAKJay9mMXKKuysrK0evVqQ57ZwRjgrTZt2qSvvvpK0tULWRMnTizlFgEw6+DBg/b/+/j4qEWLFqaXdXzy7MCBA25rF8oOx/3aqlUr08s69pHc/S23tLQ0nTp1yp6uVq2a6Zm5KlSooGbNmtnTmZmZOn78uOk2In/etN898V1QsjxxrqKfuM5msykuLs6QV7Vq1XzLemL7Hjt2zBDsbNasmekbg2rXrm2YSj4uLk6XL1/Ot6zjdzEbxJeuz99OrvQTT+DcA0fMnQwAAIqsrP3YBcqq7Oxsvfrqqzp69Kg977bbbjNM3wVcbzIyMjR58mR7+sknn8wzKwqAsunixYuGqSurVq1qmLq0MLVr1zakjxw5oq5du7qtfSi61157TVu3btWpU6eUkpKioKAghYWFqUWLFurUqZPuuOMOVapUydS6jhw5YkhHRkaabkd+fSQ/R48eNbxT15U6rtWTe8rgI0eOqGHDhi6tA0betN898V3wX3/++af27t2rffv26fz58/Lx8VGVKlVUq1YtdejQQd27d3cpIOmpcxX9xHV//fWXkpKS7OmqVasWGKj0xPYtTh3X6rn2ukSbzaajR4+qadOmTuuxWCz0lUK40k/ys2bNGq1atUoHDhzQhQsX5O/vr9DQUNWtW1cdO3ZUr169XDrnc+6BIwLsAACgyIr7YxfwZtfubt68ebM+++wzxcbG2j+rXr06T+riuvfRRx/ZbzqpW7eunnzyydJtEADTHJ+4qVmzpkvLR0REOF0fSs8nn3xiSCcmJioxMVGHDh3SN998o3feeUdDhgzR448/Lh8f5xNjnjhxwpB23O/OWK1WhYaG2l8hkpycrMTERIWGhhrKOfYdV+qQlOedpvTF4vOm/e74XVw51nGcc93mzZvz5OUeU82ePVudO3fW+PHjFRUVVej6PHWucmc/OXbsmOllyzPHc023bt1ksVjyLeuJ7evuY8qxY8fyBNgvXLiglJQUezosLMz0U/L5tel6OKa40k/y8+uvvxrSmZmZSk1NVVxcnDZs2KAZM2aoV69eGj9+vKl+xbkHjgiwAwCAIivuj13Am9x88832u9adadasmd5//31uRsF17eDBg/r444/t6YkTJ8pqtZZiiwC4IvcFYunqRWJXOAbLLl26VOw2wTOSkpI0depUbdq0SdOnT1flypULLOu4X13tJ2FhYfZAq3S13xXWd4pSh7P1wXXest+vXLlieJd8YGCgKlas6NY64LqNGzfqgQce0BtvvKG77rrLaVlPnasc8x2Xc+Zav7o2pXhaWppycnIKvYGpPNu0aZPWrFljT1ssFg0cOLDA8p7Yvp44phS3P15vxxRX+0lR5OTkaM2aNYqOjtb06dN10003OS3PuQeOCLADAIAi8cSPXcCbtGzZUo899pjuuOMO+fr6lnZzgFJjs9k0ceJE+3sO77zzTt1yyy2l3CoArkhNTTWkXb1BxvFCYe4LiSgdjRo10q233qrmzZurbt26CgoKUnp6uk6dOqU///xTX331lS5evGgvv3HjRj3zzDNasGCB/Pzyv7zouF+L208c+11J1EFfLD5v2e+Oea48aZpfm+hbBQsMDNTNN9+sm266SY0bN1ZYWJj8/PyUmJioPXv2aO3atfrzzz/t5dPT0/Xcc88pJCTE6W9IT52r3NEfc7+zOy0tTUFBQS6to7xITEzUiy++aMi77777DO+mduSJ7csxpWwpSj/JrUqVKuratavatWunRo0a2W/KSExM1I4dO7Ry5Urt3r3bUN/w4cO1dOnSfKf2v4Z+AkcE2AEAgMuK+2MXuB7FxMTo008/ldVqVc+ePUu7OUCpWb58ubZs2SJJqlSpksaPH1/KLQLgqvT0dEO6uBf/HNcHz+nSpYsefvhhtWjRIt/PmzZtqu7du+uZZ57RlClT9PXXX9s/+/PPPzV79myNHDky32Ud96urF6Id+1V+/YS+WPZ4y34vbiCFIEfhAgMDNXHiRP3v//6vAgMD83xev359/e1vf9PDDz+sjRs36rnnntP58+clXX3Kc8yYMVq7dq2qVKmS7/o9dXxwd5/31gD7tX12+vRpe15ERESea0uOPLF9i1sHxxT3KWo/kaRq1arp7bff1l133VXg33v79u01ZMgQfffdd5o4caJ9O6alpemZZ57RqlWr5O/vn++ynHvgyHvnGgEAACWiOD92AW/2xRdf6KefftJPP/2kdevW6auvvtKMGTP00EMPqVKlSrLZbNq6datGjBihMWPGKDMzs7SbDHjcuXPn9N5779nTzz77bJ530QEof1x9RZBjeZvN5s7mwAV33313gcH13IKCgvT222/rwQcfNOT/+9//Nkzn7Ywn+gl9sezxlv1e3DqQV1hYmAYMGJBvcN1R586d9cknnyg4ONied/HiRc2fP990fZ46PnAcyt+UKVO0ceNGe9rf31/Tpk1TSEiIS+spD9uXY0rRFaefNGzYUP/zP/9jKvDdp08fzZs3zxBMP378uL788kvTbeXcAwLsAADAJe4aFAHepmbNmqpdu7Zq166tyMhI3Xjjjbrjjjs0efJk/fTTT7rtttvsZVeuXKnnnnuuFFsLlI7XX39dycnJkqRmzZrp4YcfLuUWASiKgIAAQzojI8Ol5XNP1SrJVHAFZcPLL7+sWrVq2dOpqalatWpVvmUd+4njfi+MY7/Kr5/QF8seb9nvjnmufg/6lvs1bNhQ48aNM+R9+eWXBQapPHV8cHefr1SpkkvLlwcfffSRli5dak/7+PjonXfeUbt27Qpd1hPbt7h9xcxxi2NK4YrTT4qiQ4cOGjx4sCHv888/L7A85x44IsAOAABM8/SPXcBbhIaGatasWfr73/9uz1uzZo1WrlxZiq0CPOu3336zB2EsFoteeeUV+fr6lnKrABSF48W6krgQjbKpQoUKeW6Oyn3zbW7uDljkFxShL5Y93rLfHb+Hq7NPOZanb7nHfffdp8qVK9vT58+f1/79+/Mt66njgyeCs+XZ//t//0/Tp0835E2YMEF33XWXqeU9sX1Low5Xjyn0k5Lx6KOPysfnv2HSPXv2KCkpKd+ynHvgiAA7AAAwpbR+7ALews/PTxMmTDDkLVy4sJRaA3hWenq6XnnlFXv6gQceUOvWrUuxRQCKw/HdpWanCL/mwoULhnTuKX9R9nXu3NmQjo2Nzbec434tbj/J753E9MWyx1v2u5+fnyHQkZaW5tKThPStkuHv768OHToY8goKsHvq+FCcPp+enm54D3NgYKAh2FferV69WpMnTzbkjR49Wv379ze9Dk9sX3cft/LrK8Wtw7G8Nx1T3NFPiiosLExNmjSxp3NycnTw4MF8y3LugSPvOVoDAIASU5o/dgFv0rBhQ0VFRdnTMTExunjxYim2CPCMDz74QCdPnpR09SLG2LFjS7lFAIqjTp06hnR8fLxLy58+fdqQjoyMLHab4Dm5p4iXCr7A7NhPHPe7M5mZmYaLxCEhIQoNDS20juL2Rcf1wXXetN8dj02ufBfHNnGcc5+iHoNK6lzlzn7iTceg33//XePGjVNOTo49b/DgwRo2bJhL6/HE9vXEMSUsLMwQoD1//rxLTyd76zHFXf2kOByPKQU9wc65B44IsAMAAKfKwo9dwJvUrVvX/n+bzWYPOgLeKi0tTYsXL7anBw4cqEuXLikuLs7pv+zsbMN6HD93dbo8AO5TpUoVhYWF2dPnzp0zPCFWmLi4OEO6QYMGbmsbSp7VajWkC3qyqn79+ob0iRMnTNfhWNZxXbnzLRaLPe3YtwrjWL6gemCeN+13x2OTK9+F41zJqVixoiFd0DTNnjpX0U/y2rJli5555hllZWXZ8/r27asXXnjB5XV5Yvs65hfnmGKxWJweu66x2Wwu1eONfcWd/aQ4HI8pzn7XcO5BbgTYAQBAgcrKj13Am/j5+RnSBAnh7bKzsw3B8unTp6tHjx6F/ktISDCsx/HzQ4cOefqrAMilUaNG9v/n5OQoJibG9LI7duwocF0o+xyfFq1SpUq+5Ro3bmxIO+53Z3bu3GlIN2zYMN9ygYGBuuGGG+zps2fP6tSpU6bqyMzM1N69e+1pf39/r3p6tLR4035353fhOOc+Zo9BkmfOVZ7o8+XJnj17NHToUMPNDHfeeadeffXVIq3PE9u3Tp068vf3t6f37Nljepx+8uRJnTt3zp6uVatWnoDtNY7fxbF9znjbbyd395PiMHtM4dwDRwTYAQBAvsrSj13AmzgGDatWrVpKLQEAoOgc38P9119/mVouPj7eMHtL/fr1DRcrUfbt2rXLkK5Ro0a+5dq0aaPAwEDDcmYDFo796ZZbbimwbFH74s6dOw3t6dSpkyHAgqLxpv3+97//3ZDesmWLqToc22OxWPKsC0Vn9hgkeeZc5VhHUfuJ5LzPlweHDx/WkCFDdOnSJXte165d9e677xb53fKe2L4VKlRQhw4d7OnMzMw8/cxsHTfffHOBZR2PA2b7Y0ZGhuHmkEqVKql169amli2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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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       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\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",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\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-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\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",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item 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",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\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",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\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",
       "  margin-bottom: 0;\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",
       "}\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",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\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-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",
       "</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      ,  265.52955958],\n",
       "       [   3.75      ,  145.29761061],\n",
       "       [   3.75      , 2779.23378308],\n",
       "       [   3.75      ,  827.48795138]])\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-array-name'>'accept'</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-0eab5b82-e833-45a5-a169-91f2e8cc0874' class='xr-array-in' type='checkbox' checked><label for='section-0eab5b82-e833-45a5-a169-91f2e8cc0874' 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>3.75 265.5 3.75 145.3 3.75 2.779e+03 3.75 827.5</span></div><div class='xr-array-data'><pre>array([[   3.75      ,  265.52955958],\n",
       "       [   3.75      ,  145.29761061],\n",
       "       [   3.75      , 2779.23378308],\n",
       "       [   3.75      ,  827.48795138]])</pre></div></div></li><li class='xr-section-item'><input id='section-6813645e-606d-41d9-813a-9266a5287196' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6813645e-606d-41d9-813a-9266a5287196' class='xr-section-summary' >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-49e440e2-c136-4d78-9693-c69aff0a513c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49e440e2-c136-4d78-9693-c69aff0a513c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1995a306-567c-44f1-827d-6ea79a1c979c' class='xr-var-data-in' type='checkbox'><label for='data-1995a306-567c-44f1-827d-6ea79a1c979c' 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-545219f1-5426-4834-9ee7-5ba426a9e7c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-545219f1-5426-4834-9ee7-5ba426a9e7c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0185df42-add1-44fb-9ae1-a8e7106ad14b' class='xr-var-data-in' type='checkbox'><label for='data-0185df42-add1-44fb-9ae1-a8e7106ad14b' 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><li class='xr-section-item'><input id='section-bc5e4fcc-b323-4be9-a9eb-1f4daed128de' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc5e4fcc-b323-4be9-a9eb-1f4daed128de' class='xr-section-summary' >Indexes: <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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-04f1979b-7209-4277-8002-fa1c325d6c1d' class='xr-index-data-in' type='checkbox'/><label for='index-04f1979b-7209-4277-8002-fa1c325d6c1d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>accept_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bcf8ca6e-8f76-4784-af72-6ec85ce6cb57' class='xr-index-data-in' type='checkbox'/><label for='index-bcf8ca6e-8f76-4784-af72-6ec85ce6cb57' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;accept_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fea696a4-5a41-49e4-a6a8-c124edf86ecd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fea696a4-5a41-49e4-a6a8-c124edf86ecd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'accept' (chain: 4, accept_dim_0: 2)> Size: 64B\n",
       "array([[   3.75      ,  265.52955958],\n",
       "       [   3.75      ,  145.29761061],\n",
       "       [   3.75      , 2779.23378308],\n",
       "       [   3.75      ,  827.48795138]])\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Wed Dec 31 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.0\n",
      "IPython version      : 9.1.0\n",
      "\n",
      "arviz     : 0.21.0\n",
      "pymc      : 5.27.0\n",
      "matplotlib: 3.10.1\n",
      "\n",
      "Watermark: 2.5.0\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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