From adb850375f0e703fa4ccaf352a5589b45eb4c75c Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Sun, 13 Oct 2024 20:22:58 +0000
Subject: [PATCH] added new posterior predictive plots
---
.../MPD_8_1.ipynb | 3 +
.../MPD_8_2.ipynb | 2417 +++++++++++++++++
2 files changed, 2420 insertions(+)
create mode 100644 notebooks/Multi-Parameter Distributions/MPD_8_2.ipynb
diff --git a/notebooks/Multi-Parameter Distributions/MPD_8_1.ipynb b/notebooks/Multi-Parameter Distributions/MPD_8_1.ipynb
index d581ba1..1558e55 100644
--- a/notebooks/Multi-Parameter Distributions/MPD_8_1.ipynb
+++ b/notebooks/Multi-Parameter Distributions/MPD_8_1.ipynb
@@ -267,6 +267,9 @@
"execution_count": 23,
"id": "c864c1bc-5d3a-4a96-9973-f4a7a16d0582",
"metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
"tags": []
},
"outputs": [
diff --git a/notebooks/Multi-Parameter Distributions/MPD_8_2.ipynb b/notebooks/Multi-Parameter Distributions/MPD_8_2.ipynb
new file mode 100644
index 0000000..68af17d
--- /dev/null
+++ b/notebooks/Multi-Parameter Distributions/MPD_8_2.ipynb
@@ -0,0 +1,2417 @@
+{
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+ {
+ "cell_type": "markdown",
+ "id": "68bb51ea-9614-4313-9abc-bb8ed5d0e4d0",
+ "metadata": {},
+ "source": [
+ "# Example 8.2 - Posterior Predictive Check"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9875fd00-2c4a-4f01-9f23-955f55f4de8c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import pymc as pm\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.stats as st\n",
+ "import arviz as az\n",
+ "import metropolis_commands as mc\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "plt.rcParams['figure.figsize'] = [5, 3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1bda0791-6e79-4d55-8d2a-9df5f27dbb95",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>0</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>51.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>55.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>53.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>50.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>52.05</td>\n",
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+ " 0\n",
+ "0 51.06\n",
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+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
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+ ],
+ "source": [
+ "df = pd.read_csv(\"./Daten/chemical_shifts.csv\",header=None)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d59ce97d-13b5-4bf5-a723-1dcce961c073",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Auto-assigning NUTS sampler...\n",
+ "Initializing NUTS using jitter+adapt_diag...\n",
+ "Multiprocess sampling (4 chains in 4 jobs)\n",
+ "NUTS: [μ, σ, ν]\n"
+ ]
+ },
+ {
+ "data": {
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+ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 6 seconds.\n"
+ ]
+ }
+ ],
+ "source": [
+ "with pm.Model() as model_t:\n",
+ " μ = pm.Uniform('μ', 40, 75)\n",
+ " σ = pm.HalfNormal('σ', sigma=10)\n",
+ " ν = pm.Exponential('ν', 1/30)\n",
+ " y = pm.StudentT('y', nu=ν, mu=μ, sigma=σ, observed=df)\n",
+ " trace_t = pm.sample(random_seed=4591)\n",
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+ "Sampling: [y]\n"
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+ "Data variables:\n",
+ " μ (chain, draw) float64 32kB 52.77 53.09 53.08 ... 52.68 52.89 52.86\n",
+ " ν (chain, draw) float64 32kB 5.639 2.565 7.298 ... 2.844 2.335 1.648\n",
+ " σ (chain, draw) float64 32kB 2.402 2.732 2.794 ... 2.683 2.615 2.028\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T20:21:08.078707+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2\n",
+ " sampling_time: 5.622159242630005\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-e6bdd5f5-cb97-400b-bc1b-1c720bd796b6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e6bdd5f5-cb97-400b-bc1b-1c720bd796b6' 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>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39a76e17-418a-4a9b-b890-4078560de1cc' class='xr-section-summary-in' type='checkbox' checked><label for='section-39a76e17-418a-4a9b-b890-4078560de1cc' 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-cf3bb2ee-5786-4486-bc04-5500e677ec73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf3bb2ee-5786-4486-bc04-5500e677ec73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c686c9af-cb5d-4eea-b047-276b40e94d80' class='xr-var-data-in' type='checkbox'><label for='data-c686c9af-cb5d-4eea-b047-276b40e94d80' 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 5 ... 995 996 997 998 999</div><input id='attrs-75dcaf89-9d4f-439b-8a48-6334d93ebec0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75dcaf89-9d4f-439b-8a48-6334d93ebec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e61669f-039c-4e5f-ade1-14e499b39539' class='xr-var-data-in' type='checkbox'><label for='data-4e61669f-039c-4e5f-ade1-14e499b39539' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa4ea723-ca6e-4ea5-a900-83324cc7905c' class='xr-section-summary-in' type='checkbox' checked><label for='section-aa4ea723-ca6e-4ea5-a900-83324cc7905c' class='xr-section-summary' >Data variables: <span>(3)</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>μ</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.77 53.09 53.08 ... 52.89 52.86</div><input id='attrs-fe201c8d-03f8-4fd2-97e1-ca6e076a5067' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe201c8d-03f8-4fd2-97e1-ca6e076a5067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42c928f5-e7d8-4bdb-a48a-6c8881b24804' class='xr-var-data-in' type='checkbox'><label for='data-42c928f5-e7d8-4bdb-a48a-6c8881b24804' 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([[52.76574542, 53.09110799, 53.08032453, ..., 54.25124886,\n",
+ " 53.78479971, 53.43672469],\n",
+ " [52.97771349, 52.97771349, 52.45512775, ..., 53.34647094,\n",
+ " 52.6614107 , 53.40984518],\n",
+ " [53.07287094, 52.96569754, 52.89673108, ..., 53.04275087,\n",
+ " 53.18746837, 52.5633303 ],\n",
+ " [53.67556257, 53.18953976, 53.26589575, ..., 52.68116193,\n",
+ " 52.89169082, 52.86100632]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ν</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.639 2.565 7.298 ... 2.335 1.648</div><input id='attrs-ed01d647-3892-4f0e-b4c0-c130d474ef21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed01d647-3892-4f0e-b4c0-c130d474ef21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d21063f6-35a4-426a-bcfc-520756701180' class='xr-var-data-in' type='checkbox'><label for='data-d21063f6-35a4-426a-bcfc-520756701180' 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([[5.639377 , 2.5647861 , 7.29757307, ..., 4.16833139, 3.40094838,\n",
+ " 3.72721262],\n",
+ " [2.2938021 , 2.2938021 , 1.56678154, ..., 5.1382814 , 4.72276586,\n",
+ " 3.41378121],\n",
+ " [5.2519168 , 6.48902986, 2.50391413, ..., 6.48340926, 3.99989958,\n",
+ " 3.6280118 ],\n",
+ " [2.0474715 , 4.02572081, 4.61990166, ..., 2.843872 , 2.33515105,\n",
+ " 1.64832786]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>σ</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.402 2.732 2.794 ... 2.615 2.028</div><input id='attrs-4ffa314a-01dc-4914-b686-4db467bc780f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ffa314a-01dc-4914-b686-4db467bc780f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a16e02a8-9a87-41d9-9033-33f0abe88415' class='xr-var-data-in' type='checkbox'><label for='data-a16e02a8-9a87-41d9-9033-33f0abe88415' 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([[2.40201826, 2.73185478, 2.79438606, ..., 2.61754332, 2.60320465,\n",
+ " 2.59704715],\n",
+ " [1.7653379 , 1.7653379 , 1.45268899, ..., 2.82665077, 2.19975458,\n",
+ " 1.82225473],\n",
+ " [2.70768715, 2.64397995, 1.39183353, ..., 2.44678619, 2.18589669,\n",
+ " 2.29324931],\n",
+ " [2.14889325, 1.79441575, 2.32049321, ..., 2.68298998, 2.61510837,\n",
+ " 2.02786397]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3921ad44-f7ad-4644-a846-ff26c1199101' class='xr-section-summary-in' type='checkbox' ><label for='section-3921ad44-f7ad-4644-a846-ff26c1199101' 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><div></div><input id='index-4a388797-74ce-4ab7-85c2-0dd1af7c2451' class='xr-index-data-in' type='checkbox'/><label for='index-4a388797-74ce-4ab7-85c2-0dd1af7c2451' 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='int64', name='chain'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e12b1d3d-f762-48d0-b5c8-489dd6ab19ac' class='xr-index-data-in' type='checkbox'/><label for='index-e12b1d3d-f762-48d0-b5c8-489dd6ab19ac' 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",
+ " 990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+ " dtype='int64', name='draw', length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e4a5ed7-81bf-4e30-9a31-5d47838db537' class='xr-section-summary-in' type='checkbox' checked><label for='section-4e4a5ed7-81bf-4e30-9a31-5d47838db537' 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>2024-10-13T20:21:08.078707+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>5.622159242630005</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " <li class = \"xr-section-item\">\n",
+ " <input id=\"idata_posterior_predictive63aec6e7-78d2-4bf7-bcf3-422e348fee0e\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+ " <label for=\"idata_posterior_predictive63aec6e7-78d2-4bf7-bcf3-422e348fee0e\" class = \"xr-section-summary\">posterior_predictive</label>\n",
+ " <div class=\"xr-section-inline-details\"></div>\n",
+ " <div class=\"xr-section-details\">\n",
+ " <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+ " <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
+ ":root {\n",
+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+ " --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",
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+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
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+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block !important;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
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+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
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+ "}\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",
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+ ".xr-section-item input + label {\n",
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+ "}\n",
+ "\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
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+ ".xr-section-item input:enabled + label:hover {\n",
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+ " display: inline-block;\n",
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+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
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+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
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+ "}\n",
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+ ".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: 'â–¼';\n",
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+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
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+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
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+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
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+ " margin-bottom: 5px;\n",
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+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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+ "}\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",
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+ "\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'><xarray.Dataset> Size: 2MB\n",
+ "Dimensions: (chain: 4, draw: 1000, y_dim_2: 48, y_dim_3: 1)\n",
+ "Coordinates:\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+ " * y_dim_2 (y_dim_2) int64 384B 0 1 2 3 4 5 6 7 8 ... 40 41 42 43 44 45 46 47\n",
+ " * y_dim_3 (y_dim_3) int64 8B 0\n",
+ "Data variables:\n",
+ " y (chain, draw, y_dim_2, y_dim_3) float64 2MB 51.26 53.09 ... 51.23\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T20:21:13.136833+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2</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-2bbfcd1d-b12b-4b6d-92b8-12dc91a92d7d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2bbfcd1d-b12b-4b6d-92b8-12dc91a92d7d' 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>: 1000</li><li><span class='xr-has-index'>y_dim_2</span>: 48</li><li><span class='xr-has-index'>y_dim_3</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5260be48-6faa-43fe-a52b-61f7c3a63600' class='xr-section-summary-in' type='checkbox' checked><label for='section-5260be48-6faa-43fe-a52b-61f7c3a63600' class='xr-section-summary' >Coordinates: <span>(4)</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-3090db05-f58d-4128-89dd-f15bb2c6f0a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3090db05-f58d-4128-89dd-f15bb2c6f0a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01f7822f-3ffb-40eb-ab43-ca6488ba93f3' class='xr-var-data-in' type='checkbox'><label for='data-01f7822f-3ffb-40eb-ab43-ca6488ba93f3' 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 5 ... 995 996 997 998 999</div><input id='attrs-f55db357-b2d5-4a8b-a5b3-f2f15106e427' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f55db357-b2d5-4a8b-a5b3-f2f15106e427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0513c49-05b8-4c42-82b3-144307926964' class='xr-var-data-in' type='checkbox'><label for='data-c0513c49-05b8-4c42-82b3-144307926964' 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, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_2</span></div><div class='xr-var-dims'>(y_dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 42 43 44 45 46 47</div><input id='attrs-9abffb3d-055f-4310-9e7e-7457d0854d99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9abffb3d-055f-4310-9e7e-7457d0854d99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fda574f6-acf5-43df-a235-a4b79cf1bba6' class='xr-var-data-in' type='checkbox'><label for='data-fda574f6-acf5-43df-a235-a4b79cf1bba6' 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, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_3</span></div><div class='xr-var-dims'>(y_dim_3)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-529065d9-866f-4f19-9881-a34a916b30d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-529065d9-866f-4f19-9881-a34a916b30d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cecec322-2117-4336-97f2-eccc7bd7f532' class='xr-var-data-in' type='checkbox'><label for='data-cecec322-2117-4336-97f2-eccc7bd7f532' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73be03f6-c50b-4f9d-adbb-d99b5f1246a3' class='xr-section-summary-in' type='checkbox' checked><label for='section-73be03f6-c50b-4f9d-adbb-d99b5f1246a3' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(chain, draw, y_dim_2, y_dim_3)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>51.26 53.09 51.34 ... 50.19 51.23</div><input id='attrs-f8820a22-6bbd-4755-96d1-146c3642caad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8820a22-6bbd-4755-96d1-146c3642caad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15fd3edb-ecb6-4bfe-86b2-d722655d1e1d' class='xr-var-data-in' type='checkbox'><label for='data-15fd3edb-ecb6-4bfe-86b2-d722655d1e1d' 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([[[[51.25631626],\n",
+ " [53.09249049],\n",
+ " [51.33930175],\n",
+ " ...,\n",
+ " [51.26815224],\n",
+ " [55.55206667],\n",
+ " [48.94849364]],\n",
+ "\n",
+ " [[54.0758945 ],\n",
+ " [57.3463705 ],\n",
+ " [60.06134408],\n",
+ " ...,\n",
+ " [50.96961836],\n",
+ " [52.38262376],\n",
+ " [49.124465 ]],\n",
+ "\n",
+ " [[57.119909 ],\n",
+ " [53.63474631],\n",
+ " [53.30272582],\n",
+ " ...,\n",
+ "...\n",
+ " ...,\n",
+ " [49.38193118],\n",
+ " [54.06837774],\n",
+ " [54.36487714]],\n",
+ "\n",
+ " [[54.45520278],\n",
+ " [53.7297481 ],\n",
+ " [52.27242172],\n",
+ " ...,\n",
+ " [53.96314061],\n",
+ " [53.41806844],\n",
+ " [52.10219613]],\n",
+ "\n",
+ " [[55.36274136],\n",
+ " [51.39306959],\n",
+ " [51.09304073],\n",
+ " ...,\n",
+ " [55.58600929],\n",
+ " [50.18829592],\n",
+ " [51.22733394]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45d0eb81-8474-4350-be3a-2950e5324996' class='xr-section-summary-in' type='checkbox' ><label for='section-45d0eb81-8474-4350-be3a-2950e5324996' class='xr-section-summary' >Indexes: <span>(4)</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><div></div><input id='index-140799f6-62e5-429f-b205-82e053ac87ae' class='xr-index-data-in' type='checkbox'/><label for='index-140799f6-62e5-429f-b205-82e053ac87ae' 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='int64', name='chain'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01dd5c70-3bdf-41ed-abac-46a347b96276' class='xr-index-data-in' type='checkbox'/><label for='index-01dd5c70-3bdf-41ed-abac-46a347b96276' 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",
+ " 990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+ " dtype='int64', name='draw', length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5553674-63c9-429b-866e-11f8459304ee' class='xr-index-data-in' type='checkbox'/><label for='index-d5553674-63c9-429b-866e-11f8459304ee' 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, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],\n",
+ " dtype='int64', name='y_dim_2'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_3</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5710ab3d-f660-4c4b-949f-a877d5e11f0f' class='xr-index-data-in' type='checkbox'/><label for='index-5710ab3d-f660-4c4b-949f-a877d5e11f0f' 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], dtype='int64', name='y_dim_3'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-69f7914c-8425-4281-ad66-e2793c349f05' class='xr-section-summary-in' type='checkbox' checked><label for='section-69f7914c-8425-4281-ad66-e2793c349f05' class='xr-section-summary' >Attributes: <span>(4)</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>2024-10-13T20:21:13.136833+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " <li class = \"xr-section-item\">\n",
+ " <input id=\"idata_sample_statsdf23344c-78a5-49ee-8739-1bf9b9c12e48\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+ " <label for=\"idata_sample_statsdf23344c-78a5-49ee-8739-1bf9b9c12e48\" class = \"xr-section-summary\">sample_stats</label>\n",
+ " <div class=\"xr-section-inline-details\"></div>\n",
+ " <div class=\"xr-section-details\">\n",
+ " <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+ " <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+ "<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",
+ "</symbol>\n",
+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+ "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\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",
+ "}\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: 'â–º';\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: 'â–¼';\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'><xarray.Dataset> Size: 496kB\n",
+ "Dimensions: (chain: 4, draw: 1000)\n",
+ "Coordinates:\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 8kB 0 1 2 3 4 5 ... 995 996 997 998 999\n",
+ "Data variables: (12/17)\n",
+ " acceptance_rate (chain, draw) float64 32kB 0.7389 0.6169 ... 0.9138\n",
+ " diverging (chain, draw) bool 4kB False False ... False False\n",
+ " energy (chain, draw) float64 32kB 125.9 128.2 ... 127.5\n",
+ " energy_error (chain, draw) float64 32kB 0.09317 0.6892 ... -0.212\n",
+ " index_in_trajectory (chain, draw) int64 32kB -2 1 4 -1 -2 ... -2 4 -1 4\n",
+ " largest_eigval (chain, draw) float64 32kB nan nan nan ... nan nan\n",
+ " ... ...\n",
+ " process_time_diff (chain, draw) float64 32kB 0.0006863 ... 0.001323\n",
+ " reached_max_treedepth (chain, draw) bool 4kB False False ... False False\n",
+ " smallest_eigval (chain, draw) float64 32kB nan nan nan ... nan nan\n",
+ " step_size (chain, draw) float64 32kB 0.9279 0.9279 ... 0.934\n",
+ " step_size_bar (chain, draw) float64 32kB 0.764 0.764 ... 0.7202\n",
+ " tree_depth (chain, draw) int64 32kB 2 3 3 2 2 3 ... 2 2 3 3 2 3\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T20:21:08.095322+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2\n",
+ " sampling_time: 5.622159242630005\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-66419ff8-72eb-4eda-a593-92f4be172fba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66419ff8-72eb-4eda-a593-92f4be172fba' 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>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2d4363ef-ef7d-484c-aa97-429e6ae6dde0' class='xr-section-summary-in' type='checkbox' checked><label for='section-2d4363ef-ef7d-484c-aa97-429e6ae6dde0' 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-73bf4e0e-2146-4ce4-9908-841423490606' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73bf4e0e-2146-4ce4-9908-841423490606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4100804b-3c3a-417a-8ed9-4ab37d1eba2f' class='xr-var-data-in' type='checkbox'><label for='data-4100804b-3c3a-417a-8ed9-4ab37d1eba2f' 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 5 ... 995 996 997 998 999</div><input id='attrs-6435ee58-dbbe-4a18-82c4-87986b5d1a26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6435ee58-dbbe-4a18-82c4-87986b5d1a26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f10de03-b1f3-4247-a115-43b320d1bee5' class='xr-var-data-in' type='checkbox'><label for='data-7f10de03-b1f3-4247-a115-43b320d1bee5' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb778e10-016f-4624-bae5-4419aef33a84' class='xr-section-summary-in' type='checkbox' ><label for='section-bb778e10-016f-4624-bae5-4419aef33a84' class='xr-section-summary' >Data variables: <span>(17)</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'>0.7389 0.6169 ... 0.7198 0.9138</div><input id='attrs-bc237705-efcb-4d9d-bf0f-76cfad265f32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc237705-efcb-4d9d-bf0f-76cfad265f32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92071eae-306d-45d3-8466-eecb2a54ede8' class='xr-var-data-in' type='checkbox'><label for='data-92071eae-306d-45d3-8466-eecb2a54ede8' 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([[0.73886596, 0.61690038, 0.98364434, ..., 0.81036903, 1. ,\n",
+ " 1. ],\n",
+ " [0.9864567 , 0.55214811, 0.91514174, ..., 0.8513055 , 0.98736163,\n",
+ " 0.97229948],\n",
+ " [0.97990411, 1. , 0.86483481, ..., 0.98764319, 0.99580798,\n",
+ " 0.8841215 ],\n",
+ " [0.92607626, 0.99022889, 0.865829 , ..., 0.9487335 , 0.71982942,\n",
+ " 0.91375834]])</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-60b31d3e-557b-4395-910f-32e76963a0eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60b31d3e-557b-4395-910f-32e76963a0eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21eeded1-cdfa-461e-beaf-6af59160bca4' class='xr-var-data-in' type='checkbox'><label for='data-21eeded1-cdfa-461e-beaf-6af59160bca4' 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]])</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'>125.9 128.2 128.9 ... 127.6 127.5</div><input id='attrs-a8802c57-d991-47d1-80e9-1acccc1b49e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8802c57-d991-47d1-80e9-1acccc1b49e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3dab235-b3da-4bd9-b1fb-d7ab38a82987' class='xr-var-data-in' type='checkbox'><label for='data-f3dab235-b3da-4bd9-b1fb-d7ab38a82987' 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([[125.92981125, 128.21310094, 128.92784978, ..., 129.64849369,\n",
+ " 129.37972433, 126.96551196],\n",
+ " [124.68088352, 128.96621505, 127.5565366 , ..., 126.69081937,\n",
+ " 126.48963931, 125.77850101],\n",
+ " [126.79378059, 125.42304588, 127.68643444, ..., 126.06463232,\n",
+ " 124.79215591, 125.24668168],\n",
+ " [127.35243618, 127.66050066, 126.89794746, ..., 127.64349148,\n",
+ " 127.63588068, 127.53964141]])</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'>0.09317 0.6892 ... 0.2134 -0.212</div><input id='attrs-a9611baa-e95a-4db3-bd82-d07c8adda233' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9611baa-e95a-4db3-bd82-d07c8adda233' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-549a2d0d-acce-49e9-8347-86320a27f0f5' class='xr-var-data-in' type='checkbox'><label for='data-549a2d0d-acce-49e9-8347-86320a27f0f5' 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([[ 0.093168 , 0.6892388 , -0.33364296, ..., 0.33562858,\n",
+ " -0.10392587, -0.13683181],\n",
+ " [ 0.02807209, 0. , 0.20502019, ..., 0.17553273,\n",
+ " -0.18907662, 0.05867271],\n",
+ " [-0.17401545, -0.07002051, -0.27578394, ..., -0.43675051,\n",
+ " -0.08369741, 0.12479232],\n",
+ " [ 0.32074341, -0.36368011, -0.21948722, ..., -0.15864765,\n",
+ " 0.21338576, -0.21198833]])</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 1 4 -1 -2 -2 ... 1 -2 -2 4 -1 4</div><input id='attrs-33762fd0-2f0b-4c82-a988-7e34a91e8510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33762fd0-2f0b-4c82-a988-7e34a91e8510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bd9fbb0-431a-41b0-ba6f-dd28f3a311f4' class='xr-var-data-in' type='checkbox'><label for='data-8bd9fbb0-431a-41b0-ba6f-dd28f3a311f4' 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, 1, 4, ..., 2, 1, -1],\n",
+ " [ 6, 0, 3, ..., -1, -2, -5],\n",
+ " [-2, 1, -4, ..., 3, 2, 3],\n",
+ " [-3, 2, -3, ..., 4, -1, 4]])</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-09f16ebc-e43f-4ec2-860c-fbdf85eaac32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09f16ebc-e43f-4ec2-860c-fbdf85eaac32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de277537-a838-4d08-85f8-4affe736c25e' class='xr-var-data-in' type='checkbox'><label for='data-de277537-a838-4d08-85f8-4affe736c25e' 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]])</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'>-124.8 -126.7 ... -126.6 -126.1</div><input id='attrs-7dd6609e-be80-4e44-b6fa-5e1d921f5367' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7dd6609e-be80-4e44-b6fa-5e1d921f5367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4cb6102-5c7e-454d-900b-1841256dbd7c' class='xr-var-data-in' type='checkbox'><label for='data-d4cb6102-5c7e-454d-900b-1841256dbd7c' 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([[-124.81755779, -126.72003202, -125.61951405, ..., -128.98013439,\n",
+ " -126.78252316, -125.46015263],\n",
+ " [-124.52512461, -124.52512461, -127.00257984, ..., -125.79917382,\n",
+ " -124.69428256, -125.22845566],\n",
+ " [-125.18142008, -125.15422576, -126.78491643, ..., -124.77212649,\n",
+ " -124.10244197, -124.87251279],\n",
+ " [-126.98957875, -125.07489068, -124.36682231, ..., -126.39633364,\n",
+ " -126.5643574 , -126.06439179]])</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'>0.5659 1.314 ... 1.045 -0.9487</div><input id='attrs-acba4915-dcc2-4c6a-af7f-a042943011eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acba4915-dcc2-4c6a-af7f-a042943011eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39b4a1e8-e8c1-4de5-9bc4-1c75222ba330' class='xr-var-data-in' type='checkbox'><label for='data-39b4a1e8-e8c1-4de5-9bc4-1c75222ba330' 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([[ 0.56585737, 1.31377213, -0.34891784, ..., 0.33562858,\n",
+ " -0.67316672, -0.49681396],\n",
+ " [ 0.03432077, 0.95257505, 0.21594201, ..., 0.36008212,\n",
+ " -0.26174995, -0.15007968],\n",
+ " [-0.34599152, -0.1531966 , 0.56059888, ..., -0.60594081,\n",
+ " -0.08369741, 0.16530899],\n",
+ " [-0.38969789, -0.4941364 , 0.32106873, ..., -0.87410769,\n",
+ " 1.04513649, -0.94874633]])</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 3.0 ... 7.0 7.0 3.0 7.0</div><input id='attrs-6c82b67d-c3f5-4c58-816a-b4219e97714f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c82b67d-c3f5-4c58-816a-b4219e97714f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22067455-f4ef-43c3-8514-92a213476556' class='xr-var-data-in' type='checkbox'><label for='data-22067455-f4ef-43c3-8514-92a213476556' 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., 3.],\n",
+ " [7., 7., 7., ..., 7., 7., 7.],\n",
+ " [7., 3., 7., ..., 7., 7., 3.],\n",
+ " [7., 3., 3., ..., 7., 3., 7.]])</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'>0.0006856 0.001265 ... 0.001322</div><input id='attrs-2e4faa2c-e45f-498a-9287-a1198b04de63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e4faa2c-e45f-498a-9287-a1198b04de63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f3dfbc-e54c-4217-9caa-a4bf431891d8' class='xr-var-data-in' type='checkbox'><label for='data-97f3dfbc-e54c-4217-9caa-a4bf431891d8' 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([[0.00068558, 0.00126529, 0.00126944, ..., 0.00066111, 0.00066506,\n",
+ " 0.00072769],\n",
+ " [0.00131028, 0.00126734, 0.0014452 , ..., 0.00128874, 0.00125291,\n",
+ " 0.0012469 ],\n",
+ " [0.00130581, 0.00070545, 0.00124468, ..., 0.00141222, 0.00139277,\n",
+ " 0.00073799],\n",
+ " [0.00134619, 0.00074043, 0.00071187, ..., 0.00125093, 0.00067061,\n",
+ " 0.00132247]])</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'>4.148e+06 4.148e+06 ... 4.148e+06</div><input id='attrs-21d4164d-9b34-42c6-88fc-5d32228d4c5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21d4164d-9b34-42c6-88fc-5d32228d4c5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fe4ab02-5d72-4c4f-930f-5bf6f1897b6d' class='xr-var-data-in' type='checkbox'><label for='data-5fe4ab02-5d72-4c4f-930f-5bf6f1897b6d' 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([[4148336.15657837, 4148336.15743273, 4148336.15885298, ...,\n",
+ " 4148338.34624599, 4148338.34707016, 4148338.34787878],\n",
+ " [4148336.25207378, 4148336.25356206, 4148336.25499822, ...,\n",
+ " 4148338.61141029, 4148338.61285113, 4148338.61426257],\n",
+ " [4148336.26282786, 4148336.26431674, 4148336.26517888, ...,\n",
+ " 4148338.51844427, 4148338.52005753, 4148338.52162809],\n",
+ " [4148336.43478665, 4148336.43631576, 4148336.43721695, ...,\n",
+ " 4148338.70510765, 4148338.7065302 , 4148338.70735548]])</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'>0.0006863 0.001266 ... 0.001323</div><input id='attrs-9660a17d-9b09-44ee-a667-3e7d76aeec36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9660a17d-9b09-44ee-a667-3e7d76aeec36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a267a6b3-e2bb-470e-8661-9c7d78a987ae' class='xr-var-data-in' type='checkbox'><label for='data-a267a6b3-e2bb-470e-8661-9c7d78a987ae' 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([[0.00068627, 0.00126598, 0.00126974, ..., 0.00066203, 0.00066578,\n",
+ " 0.00072199],\n",
+ " [0.0013115 , 0.00126839, 0.00140767, ..., 0.00128985, 0.00125388,\n",
+ " 0.00124781],\n",
+ " [0.00130607, 0.00070634, 0.00124609, ..., 0.00141356, 0.00139411,\n",
+ " 0.00073883],\n",
+ " [0.00134624, 0.00074169, 0.00071314, ..., 0.00125193, 0.0006717 ,\n",
+ " 0.0013232 ]])</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-165dbca3-111d-43a7-8bf1-cd6f46fdc29e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-165dbca3-111d-43a7-8bf1-cd6f46fdc29e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-622092ee-5172-4bec-a57a-4dc9c4ce9e23' class='xr-var-data-in' type='checkbox'><label for='data-622092ee-5172-4bec-a57a-4dc9c4ce9e23' 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]])</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-cf341564-a8a8-4ea4-ab2a-df63fc5b97ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf341564-a8a8-4ea4-ab2a-df63fc5b97ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80c85397-13b9-4d19-bbbf-477bd762224e' class='xr-var-data-in' type='checkbox'><label for='data-80c85397-13b9-4d19-bbbf-477bd762224e' 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]])</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'>0.9279 0.9279 ... 0.934 0.934</div><input id='attrs-a268e41f-f855-4c8f-a7d4-7e6052f9771c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a268e41f-f855-4c8f-a7d4-7e6052f9771c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dabb76b7-906f-4b7b-82bd-737ef42616cf' class='xr-var-data-in' type='checkbox'><label for='data-dabb76b7-906f-4b7b-82bd-737ef42616cf' 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([[0.92794273, 0.92794273, 0.92794273, ..., 0.92794273, 0.92794273,\n",
+ " 0.92794273],\n",
+ " [0.97836881, 0.97836881, 0.97836881, ..., 0.97836881, 0.97836881,\n",
+ " 0.97836881],\n",
+ " [0.92221312, 0.92221312, 0.92221312, ..., 0.92221312, 0.92221312,\n",
+ " 0.92221312],\n",
+ " [0.93396423, 0.93396423, 0.93396423, ..., 0.93396423, 0.93396423,\n",
+ " 0.93396423]])</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'>0.764 0.764 0.764 ... 0.7202 0.7202</div><input id='attrs-c6f75814-0e1a-499b-bf70-f74c47793c1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6f75814-0e1a-499b-bf70-f74c47793c1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-396b67c6-4f91-447f-b522-990fb68df9e5' class='xr-var-data-in' type='checkbox'><label for='data-396b67c6-4f91-447f-b522-990fb68df9e5' 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([[0.76400245, 0.76400245, 0.76400245, ..., 0.76400245, 0.76400245,\n",
+ " 0.76400245],\n",
+ " [0.68507364, 0.68507364, 0.68507364, ..., 0.68507364, 0.68507364,\n",
+ " 0.68507364],\n",
+ " [0.75962243, 0.75962243, 0.75962243, ..., 0.75962243, 0.75962243,\n",
+ " 0.75962243],\n",
+ " [0.72018198, 0.72018198, 0.72018198, ..., 0.72018198, 0.72018198,\n",
+ " 0.72018198]])</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 2 2 3 2 2 ... 1 3 2 2 3 3 2 3</div><input id='attrs-a4b07f8e-2653-4302-a79f-faeb0812cf4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4b07f8e-2653-4302-a79f-faeb0812cf4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3038a3ed-a8cd-4ec0-993b-67ba4d9c2a79' class='xr-var-data-in' type='checkbox'><label for='data-3038a3ed-a8cd-4ec0-993b-67ba4d9c2a79' 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, 2],\n",
+ " [3, 3, 3, ..., 3, 3, 3],\n",
+ " [3, 2, 3, ..., 3, 3, 2],\n",
+ " [3, 2, 2, ..., 3, 2, 3]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c851a58-1f76-47a5-b84c-74d8e4dd8cb3' class='xr-section-summary-in' type='checkbox' ><label for='section-4c851a58-1f76-47a5-b84c-74d8e4dd8cb3' 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><div></div><input id='index-aa323306-b406-4528-9262-da7b265d1849' class='xr-index-data-in' type='checkbox'/><label for='index-aa323306-b406-4528-9262-da7b265d1849' 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='int64', name='chain'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8ad3406-e345-45ef-bf76-0e4a0babc846' class='xr-index-data-in' type='checkbox'/><label for='index-b8ad3406-e345-45ef-bf76-0e4a0babc846' 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",
+ " 990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+ " dtype='int64', name='draw', length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdd52200-3fba-458f-b1d5-94ff1bc539a8' class='xr-section-summary-in' type='checkbox' checked><label for='section-fdd52200-3fba-458f-b1d5-94ff1bc539a8' 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>2024-10-13T20:21:08.095322+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>5.622159242630005</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " <li class = \"xr-section-item\">\n",
+ " <input id=\"idata_observed_data2c09490d-a6b2-4afb-aeac-5f0372e4d0d0\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+ " <label for=\"idata_observed_data2c09490d-a6b2-4afb-aeac-5f0372e4d0d0\" class = \"xr-section-summary\">observed_data</label>\n",
+ " <div class=\"xr-section-inline-details\"></div>\n",
+ " <div class=\"xr-section-details\">\n",
+ " <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+ " <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+ "<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",
+ "</symbol>\n",
+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+ "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\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",
+ "}\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: 'â–º';\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: 'â–¼';\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'><xarray.Dataset> Size: 776B\n",
+ "Dimensions: (y_dim_0: 48, y_dim_1: 1)\n",
+ "Coordinates:\n",
+ " * y_dim_0 (y_dim_0) int64 384B 0 1 2 3 4 5 6 7 8 ... 40 41 42 43 44 45 46 47\n",
+ " * y_dim_1 (y_dim_1) int64 8B 0\n",
+ "Data variables:\n",
+ " y (y_dim_0, y_dim_1) float64 384B 51.06 55.12 53.73 ... 53.84 53.16\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T20:21:08.100746+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2</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-6ef31e7e-bbab-4c8a-82fa-723360e80a12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6ef31e7e-bbab-4c8a-82fa-723360e80a12' 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'>y_dim_0</span>: 48</li><li><span class='xr-has-index'>y_dim_1</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4b082690-55c1-4bc6-ad99-088af09ccf49' class='xr-section-summary-in' type='checkbox' checked><label for='section-4b082690-55c1-4bc6-ad99-088af09ccf49' 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'>y_dim_0</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 42 43 44 45 46 47</div><input id='attrs-c5442eee-5e2e-4aef-b8d5-e82b74286f94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5442eee-5e2e-4aef-b8d5-e82b74286f94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fcd3c7-3256-4c69-94b6-06acae074c26' class='xr-var-data-in' type='checkbox'><label for='data-46fcd3c7-3256-4c69-94b6-06acae074c26' 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, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_1</span></div><div class='xr-var-dims'>(y_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f84f95fa-d59e-4f71-aab7-710383d07a53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f84f95fa-d59e-4f71-aab7-710383d07a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a93ffb4-0843-4689-ae99-6dbdf331b92d' class='xr-var-data-in' type='checkbox'><label for='data-3a93ffb4-0843-4689-ae99-6dbdf331b92d' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-104c77e0-634d-4eb2-acb2-5c431aaafbe1' class='xr-section-summary-in' type='checkbox' checked><label for='section-104c77e0-634d-4eb2-acb2-5c431aaafbe1' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(y_dim_0, y_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>51.06 55.12 53.73 ... 53.84 53.16</div><input id='attrs-b5dae4e5-88b8-42cc-baff-5089e3ee01df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5dae4e5-88b8-42cc-baff-5089e3ee01df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69c5c523-f8e2-47a6-aa2f-a35daf71965f' class='xr-var-data-in' type='checkbox'><label for='data-69c5c523-f8e2-47a6-aa2f-a35daf71965f' 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([[51.06],\n",
+ " [55.12],\n",
+ " [53.73],\n",
+ " [50.24],\n",
+ " [52.05],\n",
+ " [56.4 ],\n",
+ " [48.45],\n",
+ " [52.34],\n",
+ " [55.65],\n",
+ " [51.49],\n",
+ " [51.86],\n",
+ " [63.43],\n",
+ " [53. ],\n",
+ " [56.09],\n",
+ " [51.93],\n",
+ " [52.31],\n",
+ " [52.33],\n",
+ " [57.48],\n",
+ " [57.44],\n",
+ " [55.14],\n",
+ "...\n",
+ " [54.95],\n",
+ " [50.39],\n",
+ " [52.91],\n",
+ " [51.5 ],\n",
+ " [52.68],\n",
+ " [47.72],\n",
+ " [49.73],\n",
+ " [51.82],\n",
+ " [54.99],\n",
+ " [52.84],\n",
+ " [53.19],\n",
+ " [54.52],\n",
+ " [51.46],\n",
+ " [53.73],\n",
+ " [51.61],\n",
+ " [49.81],\n",
+ " [52.42],\n",
+ " [54.3 ],\n",
+ " [53.84],\n",
+ " [53.16]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04872fdb-5cb5-4d2b-815d-7439e93c3608' class='xr-section-summary-in' type='checkbox' ><label for='section-04872fdb-5cb5-4d2b-815d-7439e93c3608' 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>y_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e8404df-6b12-497e-b8ee-eaac10ae19b8' class='xr-index-data-in' type='checkbox'/><label for='index-1e8404df-6b12-497e-b8ee-eaac10ae19b8' 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, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],\n",
+ " dtype='int64', name='y_dim_0'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9ef429d-ed28-4f58-adc8-5532364f3fca' class='xr-index-data-in' type='checkbox'/><label for='index-f9ef429d-ed28-4f58-adc8-5532364f3fca' 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], dtype='int64', name='y_dim_1'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fff6ae3-de40-4b1f-a487-f1cf78a989bf' class='xr-section-summary-in' type='checkbox' checked><label for='section-2fff6ae3-de40-4b1f-a487-f1cf78a989bf' class='xr-section-summary' >Attributes: <span>(4)</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>2024-10-13T20:21:08.100746+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
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+ "text/plain": [
+ "Inference data with groups:\n",
+ "\t> posterior\n",
+ "\t> posterior_predictive\n",
+ "\t> sample_stats\n",
+ "\t> observed_data"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pm.sample_posterior_predictive(trace_t, model=model_t, extend_inferencedata=True, random_seed=123)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d70586ed-777b-47a1-baa1-9129776e0ad3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "<Figure size 1200x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax= az.plot_ppc(trace_t, figsize=(12, 4), num_pp_samples=100, colors=[\"C1\", \"C0\", \"C1\"])\n",
+ "ax.set_xlim(40, 70)\n",
+ "plt.savefig(\"student_t.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5103a555-4fa7-42e1-ac00-29f691e791f1",
+ "metadata": {},
+ "source": [
+ "Die Verwendung der Student's $t$-Verteilung in unserem Modell führt zu prädiktiven Stichproben, die offensichtlich besser zu den Daten zu passen scheinen, sowohl was die Lage des Modus der Verteilung als auch was ihre Streuung betrifft."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b14aa2c1-796a-4a4a-95b1-346cddd24fe2",
+ "metadata": {},
+ "source": [
+ "Man beachte, dass sich die Stichproben weit von der Masse der Daten entfernen und dass einige der prädiktiven Stichproben sehr flach aussehen. Dies ist eine direkte Folge der Student's $t$-Verteilung, die erwartet, dass einzelne Datenpunkte weit vom Mittelwert oder von der Masse der Daten entfernt sind. Die Student's $t$-Verteilung ermöglicht uns eine _robustere Schätzung_, da die Ausreißer den Effekt haben, dass sie $ \\nu $ verringern (Verteilung wird flacher), anstatt den Mittelwert in ihre Richtung zu ziehen und die Standardabweichung zu erhöhen. "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
GitLab