diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "083415c8-d5bc-4920-a45a-841db7886ac8",
+   "metadata": {},
+   "source": [
+    "### DADVI linear regression example\n",
+    "\n",
+    "This is a really simple example of deterministic ADVI (DADVI) in action.\n",
+    "\n",
+    "It's based on the notebook [here](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/GLM_linear.html).\n",
+    "\n",
+    "We'll fit the model with both NUTS and DADVI and compare the result.\n",
+    "\n",
+    "Of course, this isn't the greatest use case for DADVI, since the fit with NUTS is super-fast anyway, so there's no reason to do variational inference. But we can check it to make sure they give similar results on a simple model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ea4459ce-54a4-4fd5-bafa-e5cd77f299c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymc as pm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0f44abd-6fea-4f34-ab98-677b0858327a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on PyMC v5.25.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "import pymc as pm\n",
+    "\n",
+    "from pymc import HalfCauchy, Model, Normal, sample\n",
+    "\n",
+    "print(f\"Running on PyMC v{pm.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "df7fd6b1-4961-444e-91a2-20f74654c8a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RANDOM_SEED = 8927\n",
+    "rng = np.random.default_rng(RANDOM_SEED)\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "az.style.use(\"arviz-darkgrid\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d9da527a-baec-4a67-b6f5-d769282addbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = 200\n",
+    "true_intercept = 1\n",
+    "true_slope = 2\n",
+    "\n",
+    "x = np.linspace(0, 1, size)\n",
+    "# y = a + b*x\n",
+    "true_regression_line = true_intercept + true_slope * x\n",
+    "# add noise\n",
+    "y = true_regression_line + rng.normal(scale=0.5, size=size)\n",
+    "\n",
+    "data = pd.DataFrame({\"x\": x, \"y\": y})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "886e6283-e6b9-41c0-b8fb-21c696e1de5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 711,
+       "width": 711
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(7, 7))\n",
+    "ax = fig.add_subplot(111, xlabel=\"x\", ylabel=\"y\", title=\"Generated data and underlying model\")\n",
+    "ax.plot(x, y, \"x\", label=\"sampled data\")\n",
+    "ax.plot(x, true_regression_line, label=\"true regression line\", lw=2.0)\n",
+    "plt.legend(loc=0);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d8186653-8967-4658-a52f-6440237bccdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [sigma, Intercept, slope]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1902eaec771740abb86ced51c17708b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 1 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with Model() as model:  # model specifications in PyMC are wrapped in a with-statement\n",
+    "    # Define priors\n",
+    "    sigma = HalfCauchy(\"sigma\", beta=10)\n",
+    "    intercept = Normal(\"Intercept\", 0, sigma=20)\n",
+    "    slope = Normal(\"slope\", 0, sigma=20)\n",
+    "\n",
+    "    # Define likelihood\n",
+    "    likelihood = Normal(\"y\", mu=intercept + slope * x, sigma=sigma, observed=y)\n",
+    "\n",
+    "    # Inference!\n",
+    "    # draw 3000 posterior samples using NUTS sampling\n",
+    "    idata = sample(3000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e43889e1-e5d9-4736-bf69-1f41bb2b271c",
+   "metadata": {},
+   "source": [
+    "We've done inference with NUTS. Now we can try DADVI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b18297f4-6592-4afb-a1e3-364b81039e8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This single import should be all that's needed\n",
+    "from pymc_extras.inference import fit_deterministic_advi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc6e3943-cd0d-4c66-a177-6e5f2a34c5a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'f': 713.4534726015399, ||grad(f)||: 967.1327633132877\n",
+      "'f': 479.931132929988, ||grad(f)||: 321.38918482649177\n",
+      "'f': 415.0696415398314, ||grad(f)||: 121.08420341297919\n",
+      "'f': 337.7224486499685, ||grad(f)||: 162.75528032449952\n",
+      "'f': 504.02428287446673, ||grad(f)||: 981.96731906951\n",
+      "'f': 264.4594240322506, ||grad(f)||: 133.6422132575744\n",
+      "'f': 238.3620698481016, ||grad(f)||: 295.47412016993616\n",
+      "'f': 202.3872063826712, ||grad(f)||: 78.51034098808502\n",
+      "'f': 191.15958110793596, ||grad(f)||: 37.53448393652803\n",
+      "'f': 184.84485574860963, ||grad(f)||: 107.08693948173128\n",
+      "'f': 178.74616664207306, ||grad(f)||: 35.669339324740314\n",
+      "'f': 173.99800346020643, ||grad(f)||: 8.829578264006848\n",
+      "'f': 173.90207541922908, ||grad(f)||: 1.0160571789804076\n",
+      "'f': 173.8315687218876, ||grad(f)||: 0.2777895329277002\n",
+      "'f': 173.82759779415377, ||grad(f)||: 0.057755540352186614\n",
+      "'f': 173.82750584115203, ||grad(f)||: 0.0016314353337682263\n",
+      "'f': 173.82750529818853, ||grad(f)||: 2.2617180758073807e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can fit DADVI now. It prints some progress of its convergence -- note we could change this\n",
+    "# to look different / remove it.\n",
+    "# Note that unlike regular ADVI, deterministic ADVI converges automatically. This is one of its key advantages.\n",
+    "with model:\n",
+    "    dadvi_res = fit_deterministic_advi()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c47cb209-907b-4873-a3bf-39bff5578502",
+   "metadata": {},
+   "outputs": [
+    {
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+       "\n",
+       ".xr-header > div,\n",
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+       "\n",
+       ".xr-obj-type,\n",
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+       ".xr-sections {\n",
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+       "}\n",
+       "\n",
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+       "  font-weight: 500;\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "  content: \"▼\";\n",
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+       "\n",
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+       "\n",
+       ".xr-section-summary,\n",
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+       "\n",
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+       "  content: \")\";\n",
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+       "  padding-right: 5px;\n",
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+       "\n",
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+       "  padding: 0 1px;\n",
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+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
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+       "  padding: 0;\n",
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+       "  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",
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+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 32kB\n",
+       "Dimensions:    (chain: 1, draw: 1000)\n",
+       "Coordinates:\n",
+       "  * chain      (chain) int64 8B 0\n",
+       "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+       "Data variables:\n",
+       "    Intercept  (chain, draw) float64 8kB 1.024 1.132 1.072 ... 1.06 0.9415\n",
+       "    slope      (chain, draw) float64 8kB 1.917 2.003 1.989 ... 2.093 2.046 2.052\n",
+       "    sigma      (chain, draw) float64 8kB 0.5302 0.4897 0.5021 ... 0.5441 0.4952\n",
+       "Attributes:\n",
+       "    created_at:     2025-08-14T09:59:22.382706+00:00\n",
+       "    arviz_version:  0.22.0</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-82bad586-956e-4b0e-9d31-6db45a728882' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-82bad586-956e-4b0e-9d31-6db45a728882' 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>: 1</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-cc873bdf-86c1-4455-b034-5e91e967ba6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc873bdf-86c1-4455-b034-5e91e967ba6e' 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</div><input id='attrs-2e2ac284-da5f-43a5-811a-08a3bebb5bf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e2ac284-da5f-43a5-811a-08a3bebb5bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba5dd1a-fade-495d-9ad2-be87a917956b' class='xr-var-data-in' type='checkbox'><label for='data-0ba5dd1a-fade-495d-9ad2-be87a917956b' 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><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-d628e1d0-d82a-4831-9f08-fe3175a0e53b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d628e1d0-d82a-4831-9f08-fe3175a0e53b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f26e5a0-bd0a-4859-bfbf-b7b066ef242e' class='xr-var-data-in' type='checkbox'><label for='data-9f26e5a0-bd0a-4859-bfbf-b7b066ef242e' 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], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-030dd6d7-4e23-44a3-bfc5-994bf3096062' class='xr-section-summary-in' type='checkbox'  checked><label for='section-030dd6d7-4e23-44a3-bfc5-994bf3096062' 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>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.024 1.132 1.072 ... 1.06 0.9415</div><input id='attrs-cfce0f01-6259-4bfb-8d33-daaa3e652aa1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfce0f01-6259-4bfb-8d33-daaa3e652aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bc4f49-f687-4fcf-8ddf-ccc4b75f8206' class='xr-var-data-in' type='checkbox'><label for='data-53bc4f49-f687-4fcf-8ddf-ccc4b75f8206' 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([[1.02423358, 1.13214564, 1.072478  , 0.99841007, 1.04827592,\n",
+       "        1.01482212, 1.00686333, 1.0378902 , 1.0283221 , 0.98379442,\n",
+       "        1.03198199, 1.05201041, 1.07242427, 1.07454867, 0.94809642,\n",
+       "        1.02848707, 1.00260019, 1.02610458, 1.06883805, 1.07276731,\n",
+       "        1.02409935, 1.06319896, 1.01054579, 1.08466965, 1.02608234,\n",
+       "        1.12921922, 1.08256285, 1.04204586, 1.02490851, 1.10998724,\n",
+       "        1.06612953, 1.11989228, 1.06605638, 0.92315095, 0.9866524 ,\n",
+       "        1.18172176, 1.00836459, 1.01670677, 1.03160834, 0.99736694,\n",
+       "        0.99251318, 1.06833068, 1.08554496, 0.95350888, 0.99044707,\n",
+       "        1.09959851, 1.07221878, 1.03288865, 1.21815275, 1.02359065,\n",
+       "        1.07765238, 0.93752456, 1.10474129, 1.02262079, 1.02152724,\n",
+       "        1.08578299, 1.26163946, 0.94191896, 1.02697613, 1.02959188,\n",
+       "        1.11616078, 1.07308073, 0.96985303, 1.07873855, 0.98659023,\n",
+       "        1.14075991, 1.10028964, 1.02372132, 1.09171603, 1.08464893,\n",
+       "        0.94902426, 0.97527543, 1.01708894, 1.00200698, 1.05244998,\n",
+       "        0.97587417, 1.10305835, 1.09727215, 0.992403  , 1.14188873,\n",
+       "        0.97697696, 1.0301139 , 1.01365668, 1.02853078, 1.04910016,\n",
+       "        1.04916673, 1.06371857, 1.08100031, 1.02091666, 0.99710785,\n",
+       "        0.95942206, 1.01184389, 1.10094271, 1.02723867, 0.93995075,\n",
+       "        1.03878949, 0.98541613, 1.00822683, 1.01612199, 1.02049984,\n",
+       "...\n",
+       "        1.04343402, 1.08630445, 1.03480269, 0.92914828, 1.10605693,\n",
+       "        1.0164609 , 1.02400983, 1.20819081, 1.23374511, 1.09730258,\n",
+       "        0.97090626, 1.00648394, 1.0233964 , 1.12086542, 1.10114055,\n",
+       "        1.00309621, 1.06004915, 0.92752479, 1.10954258, 1.03301626,\n",
+       "        1.11615819, 0.98744769, 1.02026043, 1.1153196 , 0.99663706,\n",
+       "        1.16998398, 1.03583563, 1.11559131, 1.01989872, 1.06736821,\n",
+       "        0.96846236, 1.05644294, 1.04882904, 1.02808556, 0.96054129,\n",
+       "        1.00676139, 1.0603263 , 1.03447109, 1.10776001, 1.06860415,\n",
+       "        1.02088386, 0.9987875 , 1.04225094, 1.09002115, 1.02716016,\n",
+       "        1.04482285, 1.11519222, 1.06931526, 1.13311052, 0.98383476,\n",
+       "        1.06530887, 0.95936957, 1.02754557, 1.01991861, 0.94289119,\n",
+       "        1.10781833, 1.09270573, 1.05499586, 1.1113207 , 1.10407635,\n",
+       "        1.00620247, 1.05778944, 0.94591702, 1.04000685, 1.00446716,\n",
+       "        0.95204823, 1.03531636, 1.13389779, 1.03389275, 0.94733869,\n",
+       "        1.06619758, 1.10565022, 1.03408208, 0.94122729, 1.04245624,\n",
+       "        0.97263879, 1.00927691, 0.99928997, 1.11684691, 0.99609668,\n",
+       "        1.05751935, 1.13451328, 0.99478621, 1.08727973, 0.98795396,\n",
+       "        1.02913718, 1.17274048, 1.07302921, 1.07379724, 1.07174714,\n",
+       "        1.13900459, 1.0109294 , 0.94462211, 0.96500562, 1.01506402,\n",
+       "        1.02960575, 1.08311952, 0.95107408, 1.05994956, 0.94154183]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.917 2.003 1.989 ... 2.046 2.052</div><input id='attrs-39b252a8-a016-4ad6-bf12-625ac6634920' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39b252a8-a016-4ad6-bf12-625ac6634920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb5b1645-2f72-498a-95c8-027c85f095be' class='xr-var-data-in' type='checkbox'><label for='data-fb5b1645-2f72-498a-95c8-027c85f095be' 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([[1.91697266, 2.00334667, 1.98892008, 2.21246449, 2.09548297,\n",
+       "        2.13791721, 2.00092046, 1.99353576, 2.01696503, 2.04927131,\n",
+       "        1.89705003, 2.15023709, 2.05951416, 2.11096041, 2.1574465 ,\n",
+       "        2.06271442, 2.12626441, 2.05454434, 2.01716359, 1.93549484,\n",
+       "        2.05687313, 2.06259998, 2.09449797, 2.11572765, 1.98619852,\n",
+       "        2.0534112 , 2.04687089, 2.14280237, 2.15074432, 2.08292706,\n",
+       "        2.07313959, 2.12896403, 2.13781914, 2.08527514, 2.03922289,\n",
+       "        2.08891919, 2.03782409, 2.02008001, 2.01289906, 1.96121384,\n",
+       "        2.03792215, 2.14376328, 2.13352274, 2.0262721 , 2.04728544,\n",
+       "        2.11598492, 1.96484127, 1.92054547, 2.17912692, 2.10604964,\n",
+       "        2.04428335, 2.08777197, 2.17573851, 2.12211387, 2.06784255,\n",
+       "        2.09270497, 2.16172249, 2.11116528, 2.0859719 , 2.13621564,\n",
+       "        2.02920448, 2.10159269, 2.08347721, 2.11011689, 1.97801184,\n",
+       "        2.09642421, 1.98271113, 2.15969911, 2.10383467, 2.05227256,\n",
+       "        2.03844841, 2.07552461, 2.09639081, 2.20262033, 2.06354795,\n",
+       "        2.08422265, 2.08555662, 2.01532663, 1.8923331 , 2.05439962,\n",
+       "        2.05402556, 2.09614278, 2.09376097, 2.09544067, 1.90107797,\n",
+       "        2.05047342, 2.06520958, 2.00263974, 2.05900631, 1.96361798,\n",
+       "        1.98831187, 2.036075  , 2.10791378, 1.95734785, 2.05936628,\n",
+       "        1.95004469, 2.12812067, 1.98806304, 1.97977537, 2.03313343,\n",
+       "...\n",
+       "        2.05530695, 2.00217808, 2.11441924, 2.06249682, 2.05009886,\n",
+       "        1.98657164, 2.09668262, 2.03196947, 2.04352393, 2.04953399,\n",
+       "        2.00931414, 2.12690864, 2.1018454 , 2.1204206 , 2.09961675,\n",
+       "        2.07535717, 2.04098068, 2.1205835 , 2.14888293, 2.03443908,\n",
+       "        2.1732742 , 2.08582487, 2.02294894, 1.99318913, 2.03235507,\n",
+       "        2.10177151, 2.02774304, 2.07596736, 2.06460154, 2.1751096 ,\n",
+       "        2.10421417, 2.10006932, 2.17373393, 2.02043598, 2.11959153,\n",
+       "        1.92492828, 2.05951315, 2.0348166 , 2.03930556, 1.99986352,\n",
+       "        1.87102165, 2.0902266 , 2.08207058, 2.08337471, 2.12790317,\n",
+       "        1.9673689 , 1.91446766, 1.89179225, 2.10400844, 2.05124513,\n",
+       "        1.99217333, 2.10397891, 1.98707775, 1.93347469, 1.99235129,\n",
+       "        2.03588954, 2.07867537, 1.92756295, 2.13468686, 2.10931335,\n",
+       "        2.13600089, 2.08006471, 2.24951764, 2.06121473, 2.09622105,\n",
+       "        2.04410602, 2.03698884, 2.20533904, 2.03162615, 2.20700731,\n",
+       "        2.19161057, 2.04844913, 1.98522305, 2.07154886, 2.11393931,\n",
+       "        2.06769412, 1.96168876, 2.02024033, 1.98970737, 2.07018536,\n",
+       "        1.98646095, 2.0011341 , 2.22943759, 2.05596641, 1.92291756,\n",
+       "        1.99934292, 1.9652415 , 2.13451806, 2.04079166, 2.16918646,\n",
+       "        2.05669288, 2.14205927, 2.1223799 , 2.08985125, 2.12973614,\n",
+       "        2.03145339, 2.05918212, 2.09295493, 2.0457824 , 2.05163278]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5302 0.4897 ... 0.5441 0.4952</div><input id='attrs-ff3920cb-48a1-4d98-aa96-10c2c6aeffaa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff3920cb-48a1-4d98-aa96-10c2c6aeffaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb377af-71a1-4524-8bfe-41a2256b151d' class='xr-var-data-in' type='checkbox'><label for='data-dbb377af-71a1-4524-8bfe-41a2256b151d' 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([[0.53015255, 0.48974337, 0.50214987, 0.54506267, 0.50507724,\n",
+       "        0.53105185, 0.5317347 , 0.5377762 , 0.52581713, 0.4981341 ,\n",
+       "        0.58849647, 0.54057837, 0.51142625, 0.52396477, 0.57521445,\n",
+       "        0.54655447, 0.53364766, 0.52248039, 0.5634492 , 0.53678421,\n",
+       "        0.52831535, 0.48425602, 0.49772001, 0.51590304, 0.53313106,\n",
+       "        0.47076414, 0.48440758, 0.55190266, 0.49896913, 0.52042571,\n",
+       "        0.5454315 , 0.49098492, 0.52627446, 0.56045215, 0.54212621,\n",
+       "        0.5779551 , 0.51754998, 0.5100735 , 0.51912387, 0.59712898,\n",
+       "        0.54210897, 0.55126015, 0.50794753, 0.52874732, 0.54606393,\n",
+       "        0.4967436 , 0.53459334, 0.49529004, 0.55559647, 0.54867529,\n",
+       "        0.5316045 , 0.54652462, 0.5249046 , 0.50191756, 0.50193375,\n",
+       "        0.53740496, 0.55181244, 0.54044538, 0.51068534, 0.51730578,\n",
+       "        0.53890784, 0.55202246, 0.52337899, 0.55534095, 0.51462914,\n",
+       "        0.53591017, 0.52359439, 0.52793408, 0.50578853, 0.5063827 ,\n",
+       "        0.53959348, 0.51484645, 0.53883154, 0.52399437, 0.51136522,\n",
+       "        0.5597166 , 0.55385763, 0.53618329, 0.52075431, 0.49152059,\n",
+       "        0.48458167, 0.52765521, 0.54078442, 0.5246824 , 0.53538894,\n",
+       "        0.54072892, 0.57977916, 0.51129132, 0.50734774, 0.52728755,\n",
+       "        0.51537562, 0.51687298, 0.5657848 , 0.56909803, 0.53805612,\n",
+       "        0.53230628, 0.56231002, 0.58054123, 0.5386571 , 0.52733389,\n",
+       "...\n",
+       "        0.51002185, 0.52671278, 0.55330783, 0.54956578, 0.48170754,\n",
+       "        0.52523513, 0.50683404, 0.53390153, 0.53196713, 0.5088871 ,\n",
+       "        0.54630742, 0.51664701, 0.5267468 , 0.47639725, 0.55933856,\n",
+       "        0.54263262, 0.5265029 , 0.53952616, 0.57484308, 0.55091962,\n",
+       "        0.52467183, 0.54330671, 0.51847638, 0.52217469, 0.55500906,\n",
+       "        0.59339147, 0.56652856, 0.55112975, 0.54379306, 0.53063737,\n",
+       "        0.50080311, 0.49916256, 0.4686819 , 0.51950147, 0.51119591,\n",
+       "        0.53359993, 0.52232431, 0.525242  , 0.56194112, 0.51220094,\n",
+       "        0.55890824, 0.54051912, 0.50781342, 0.55823819, 0.5425651 ,\n",
+       "        0.53192567, 0.53188257, 0.57019311, 0.52980983, 0.54080906,\n",
+       "        0.49986879, 0.58095159, 0.53010561, 0.53766203, 0.49737649,\n",
+       "        0.53047298, 0.54496468, 0.53772425, 0.55048247, 0.53958187,\n",
+       "        0.56532771, 0.52216727, 0.52190955, 0.5172137 , 0.49751614,\n",
+       "        0.53719759, 0.53703491, 0.53174331, 0.5282638 , 0.48961309,\n",
+       "        0.52261922, 0.56817977, 0.562544  , 0.54562333, 0.51747933,\n",
+       "        0.51146554, 0.52985844, 0.5494802 , 0.56405881, 0.53818859,\n",
+       "        0.54697677, 0.52404384, 0.57143866, 0.54777914, 0.52836731,\n",
+       "        0.58833479, 0.52167064, 0.53627105, 0.49238545, 0.52246454,\n",
+       "        0.51645733, 0.56248672, 0.5043927 , 0.53806378, 0.58094705,\n",
+       "        0.55577484, 0.55521349, 0.48293466, 0.54412583, 0.49516182]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c9f139b-6642-487d-a3a8-d721f59bcdd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c9f139b-6642-487d-a3a8-d721f59bcdd6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c759756a-d462-4969-81cc-d49014499794' class='xr-index-data-in' type='checkbox'/><label for='index-c759756a-d462-4969-81cc-d49014499794' 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=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-337053fa-dc27-4edc-80e3-8a931c6afb32' class='xr-index-data-in' type='checkbox'/><label for='index-337053fa-dc27-4edc-80e3-8a931c6afb32' 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=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a981a3f3-78e9-48d7-aa42-fcbe7efaa939' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a981a3f3-78e9-48d7-aa42-fcbe7efaa939' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-14T09:59:22.382706+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
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+       "  --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",
+       "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-border-color: #1F1F1F;\n",
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+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\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",
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+       "\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",
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+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
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+       "}\n",
+       "\n",
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
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+       "  padding-left: 0 !important;\n",
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+       "\n",
+       ".xr-sections.group-sections {\n",
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+       "}\n",
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+       ".xr-section-item {\n",
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+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
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+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
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+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
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+       "\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".xr-section-summary > span {\n",
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+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "  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",
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+       ".xr-section-summary-in:checked + label:before {\n",
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+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
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+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
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+       "  padding-bottom: 4px;\n",
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+       "\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",
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+       "  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",
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+       "\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",
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+       "\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",
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+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
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+       "\n",
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+       ".xr-preview,\n",
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+       ".xr-var-name:hover,\n",
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+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
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+       "\n",
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+       "}\n",
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+       "}\n",
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+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
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+       "  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",
+       "  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",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DADVI returns draws like MCMC\n",
+    "dadvi_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2d911a25-5970-4c30-bd2e-21aa0fa74217",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_44223/2054181913.py:18: UserWarning: The figure layout has changed to tight\n",
+      "  f.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 790,
+       "width": 790
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "f, ax = plt.subplots(3, 1)\n",
+    "\n",
+    "sns.kdeplot(idata.posterior.Intercept.values.reshape(-1), ax=ax[0], label='MCMC')\n",
+    "sns.kdeplot(dadvi_res.posterior.Intercept.values.reshape(-1), ax=ax[0], label='DADVI')\n",
+    "\n",
+    "sns.kdeplot(idata.posterior.slope.values.reshape(-1), ax=ax[1], label='MCMC')\n",
+    "sns.kdeplot(dadvi_res.posterior.slope.values.reshape(-1), ax=ax[1], label='DADVI')\n",
+    "\n",
+    "sns.kdeplot(idata.posterior.sigma.values.reshape(-1), ax=ax[2], label='MCMC')\n",
+    "sns.kdeplot(dadvi_res.posterior.sigma.values.reshape(-1), ax=ax[2], label='DADVI')\n",
+    "\n",
+    "for cur_ax in ax:\n",
+    "    cur_ax.legend()\n",
+    "\n",
+    "f.set_size_inches(8, 8)\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "853f8ee0-5730-47c8-ba6b-d2fed86677b4",
+   "metadata": {},
+   "source": [
+    "We can see that the posteriors don't align perfectly. DADVI does a pretty good job matching the means, but underestimates the variance somewhat. However, often DADVI is much faster than MCMC, and so this is a tradeoff that can make sense.\n",
+    "\n",
+    "Note that DADVI as explained in the [paper](https://jmlr.org/papers/volume25/23-1015/23-1015.pdf) has further tools to make variances more accurate, which we plan to add to PyMC later on. For now, its key advantage is that it converges automatically and reliably, removing one headache from ADVI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78df242e-a266-4dda-8ca4-43b85bc92327",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pymc_extras/inference/__init__.py b/pymc_extras/inference/__init__.py
index a536f91e..ad17211c 100644
--- a/pymc_extras/inference/__init__.py
+++ b/pymc_extras/inference/__init__.py
@@ -16,5 +16,16 @@
 from pymc_extras.inference.laplace_approx.find_map import find_MAP
 from pymc_extras.inference.laplace_approx.laplace import fit_laplace
 from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
+from pymc_extras.inference.deterministic_advi.api import (
+    fit_deterministic_advi as fit_deterministic_advi_jax,
+)
+from pymc_extras.inference.deterministic_advi.pytensor import fit_deterministic_advi
 
-__all__ = ["fit", "fit_pathfinder", "fit_laplace", "find_MAP"]
+__all__ = [
+    "fit",
+    "fit_pathfinder",
+    "fit_laplace",
+    "find_MAP",
+    "fit_deterministic_advi",
+    "fit_deterministic_advi_jax",
+]
diff --git a/pymc_extras/inference/deterministic_advi/__init__.py b/pymc_extras/inference/deterministic_advi/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/pymc_extras/inference/deterministic_advi/api.py b/pymc_extras/inference/deterministic_advi/api.py
new file mode 100644
index 00000000..2f23c153
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/api.py
@@ -0,0 +1,163 @@
+from typing import Callable, Dict
+
+import numpy as np
+import pymc
+import arviz as az
+from jax import vmap
+
+from pymc_extras.inference.deterministic_advi.jax import build_dadvi_funs
+from pymc_extras.inference.deterministic_advi.pymc_to_jax import (
+    get_jax_functions_from_pymc,
+    transform_dadvi_draws,
+)
+from pymc_extras.inference.deterministic_advi.core import (
+    find_dadvi_optimum,
+    get_dadvi_draws,
+    DADVIFuns,
+)
+from pymc_extras.inference.deterministic_advi.utils import opt_callback_fun
+
+
+class DADVIResult:
+    def __init__(
+        self,
+        fixed_draws: np.ndarray,
+        var_params: np.ndarray,
+        unflattening_fun: Callable[[np.ndarray], Dict[str, np.ndarray]],
+        dadvi_funs: DADVIFuns,
+        pymc_model: pymc.Model,  # TODO Check the type here
+    ):
+
+        self.fixed_draws = fixed_draws
+        self.var_params = var_params
+        self.unflattening_fun = unflattening_fun
+        self.dadvi_funs = dadvi_funs
+        self.n_params = self.fixed_draws.shape[1]
+        self.pymc_model = pymc_model
+
+    def get_posterior_means(self) -> Dict[str, np.ndarray]:
+        """
+        Returns a dictionary with posterior means for all parameters.
+        """
+
+        means = np.split(self.var_params, 2)[0]
+        return self.unflattening_fun(means)
+
+    def get_posterior_standard_deviations_mean_field(self) -> Dict[str, np.ndarray]:
+        """
+        Returns a dictionary with posterior standard deviations (not LRVB-corrected, but mean field).
+        """
+
+        log_sds = np.split(self.var_params, 2)[1]
+        sds = np.exp(log_sds)
+        return self.unflattening_fun(sds)
+
+    def get_posterior_draws_mean_field(
+        self,
+        n_draws: int = 1000,
+        seed: int = 2,
+        transform_draws: bool = True,
+    ) -> Dict[str, np.ndarray]:
+        """
+        Returns a dictionary with draws from the posterior.
+        """
+
+        np.random.seed(seed)
+        z = np.random.randn(n_draws, self.n_params)
+        dadvi_draws_flat = get_dadvi_draws(self.var_params, z)
+
+        if transform_draws:
+
+            dadvi_draws = transform_dadvi_draws(
+                self.pymc_model,
+                dadvi_draws_flat,
+                self.unflattening_fun,
+                add_chain_dim=True,
+            )
+
+        else:
+
+            dadvi_draws = vmap(self.unflattening_fun)(dadvi_draws_flat)
+
+        return dadvi_draws
+
+    def compute_function_on_mean_field_draws(
+        self,
+        function_to_run: Callable[[Dict], np.ndarray],
+        n_draws: int = 1000,
+        seed: int = 2,
+    ):
+        dadvi_dict = self.get_posterior_draws_mean_field(n_draws, seed)
+
+        return vmap(function_to_run)(dadvi_dict)
+
+
+def fit_deterministic_advi(model=None, num_fixed_draws=30, seed=2):
+    """
+    Does inference using deterministic ADVI (automatic differentiation
+    variational inference).
+
+    For full details see the paper cited in the references:
+    https://www.jmlr.org/papers/v25/23-1015.html
+
+    Parameters
+    ----------
+    model : pm.Model
+        The PyMC model to be fit. If None, the current model context is used.
+
+    num_fixed_draws : int
+        The number of fixed draws to use for the optimisation. More
+        draws will result in more accurate estimates, but also
+        increase inference time. Usually, the default of 30 is a good
+        tradeoff.between speed and accuracy.
+
+    seed: int
+        The random seed to use for the fixed draws. Running the optimisation
+        twice with the same seed should arrive at the same result.
+
+    Returns
+    -------
+    :class:`~arviz.InferenceData`
+        The inference data containing the results of the DADVI algorithm.
+
+    References
+    ----------
+    Giordano, R., Ingram, M., & Broderick, T. (2024). Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. Journal of Machine Learning Research, 25(18), 1–39.
+
+
+    """
+
+    model = pymc.modelcontext(model) if model is None else model
+
+    np.random.seed(seed)
+
+    jax_funs = get_jax_functions_from_pymc(model)
+    dadvi_funs = build_dadvi_funs(jax_funs["log_posterior_fun"])
+
+    opt_callback_fun.opt_sequence = []
+
+    init_means = np.zeros(jax_funs["n_params"])
+    init_log_vars = np.zeros(jax_funs["n_params"]) - 3
+    init_var_params = np.concatenate([init_means, init_log_vars])
+    zs = np.random.randn(num_fixed_draws, jax_funs["n_params"])
+    opt = find_dadvi_optimum(
+        init_params=init_var_params,
+        zs=zs,
+        dadvi_funs=dadvi_funs,
+        verbose=True,
+        callback_fun=opt_callback_fun,
+    )
+
+    dadvi_result = DADVIResult(
+        fixed_draws=zs,
+        var_params=opt["opt_result"].x,
+        unflattening_fun=jax_funs["unflatten_fun"],
+        dadvi_funs=dadvi_funs,
+        pymc_model=model,
+    )
+
+    # Get draws and turn into arviz format expected
+    draws = dadvi_result.get_posterior_draws_mean_field(transform_draws=True)
+    az_draws = az.convert_to_inference_data(draws)
+
+    return az_draws
diff --git a/pymc_extras/inference/deterministic_advi/core.py b/pymc_extras/inference/deterministic_advi/core.py
new file mode 100644
index 00000000..85e1eca4
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/core.py
@@ -0,0 +1,123 @@
+"""
+Core computations for DADVI.
+"""
+
+from typing import NamedTuple, Callable, Optional, Dict
+
+from scipy.sparse.linalg import LinearOperator
+
+import numpy as np
+from pymc_extras.inference.deterministic_advi.optimization import optimize_with_hvp
+
+
+class DADVIFuns(NamedTuple):
+    """
+    This NamedTuple holds the functions required to run DADVI.
+
+    Args:
+    kl_est_and_grad_fun: Function of eta [variational parameters] and zs [draws].
+        zs should have shape [M, D], where M is number of fixed draws and D is
+        problem dimension. Returns a tuple whose first argument is the estimate
+        of the KL divergence, and the second is its gradient w.r.t. eta.
+    kl_est_hvp_fun: Function of eta, zs, and b, a vector to compute the hvp
+        with. This should return a vector -- the result of the hvp with b.
+    """
+
+    kl_est_and_grad_fun: Callable[[np.ndarray, np.ndarray], np.ndarray]
+    kl_est_hvp_fun: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], np.ndarray]]
+
+
+def find_dadvi_optimum(
+    init_params: np.ndarray,
+    zs: np.ndarray,
+    dadvi_funs: DADVIFuns,
+    opt_method: str = "trust-ncg",
+    callback_fun: Optional[Callable] = None,
+    verbose: bool = False,
+) -> Dict:
+    """
+    Optimises the DADVI objective.
+
+    Args:
+    init_params: The initial variational parameters to use. This should be a
+        vector of length 2D, where D is the problem dimension. The first D
+        entries specify the variational means, while the last D specify the log
+        standard deviations.
+    zs: The fixed draws to use in the optimisation. They must be of shape
+        [M, D], where D is the problem dimension and M is the number of fixed
+        draws.
+    dadvi_funs: The objective to optimise. See the definition of DADVIFuns for
+        more information. The kl_est_and_grad_fun is required for optimisation;
+        the kl_est_hvp_fun is needed only for some optimisers.
+    opt_method: The optimisation method to use. This must be one of the methods
+        listed for scipy.optimize.minimize
+        [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html].
+        Defaults to trust-ncg, which requires the hvp to be available. For
+        gradient-only optimisation, L-BFGS-B generally works well.
+    callback_fun: If provided, this callback function is passed to
+        scipy.optimize.minimize. See that function's documentation for more.
+    verbose: If True, prints the progress of the optimisation by showing the
+        value and gradient norm at each iteration of the optimizer.
+
+    Returns:
+    A dictionary with entries "opt_result", containing the results of running
+    scipy.optimize.minimize, and "evaluation_count", containing the number of
+    times the hvp and gradient functions were called.
+    """
+
+    val_and_grad_fun = lambda var_params: dadvi_funs.kl_est_and_grad_fun(var_params, zs)
+    hvp_fun = (
+        None
+        if dadvi_funs.kl_est_hvp_fun is None
+        else lambda var_params, b: dadvi_funs.kl_est_hvp_fun(var_params, zs, b)
+    )
+
+    opt_result, eval_count = optimize_with_hvp(
+        val_and_grad_fun,
+        hvp_fun,
+        init_params,
+        opt_method=opt_method,
+        callback_fun=callback_fun,
+        verbose=verbose,
+    )
+
+    to_return = {
+        "opt_result": opt_result,
+        "evaluation_count": eval_count,
+    }
+
+    # TODO: Here I originally had a Newton step check to assess
+    # convergence. Could add this back in.
+
+    return to_return
+
+
+def get_dadvi_draws(var_params: np.ndarray, zs: np.ndarray) -> np.ndarray:
+    """
+    Computes draws from the mean-field variational approximation given
+    variational parameters and a matrix of fixed draws.
+
+    Args:
+        var_params: A vector of shape 2D, the first D entries specifying the
+            means for the D model parameters, and the last D the log standard
+            deviations.
+        zs: A matrix of shape [N, D], containing the draws to use to sample the
+            variational approximation.
+
+    Returns:
+    A matrix of shape [N, D] containing N draws from the variational
+    approximation.
+    """
+
+    # TODO: Could use JAX here
+    means, log_sds = np.split(var_params, 2)
+    sds = np.exp(log_sds)
+
+    draws = means.reshape(1, -1) + zs * sds.reshape(1, -1)
+
+    return draws
+
+
+# TODO -- I think the functions above cover the basic functionality of
+# fixed-draw ADVI. But I have not yet included the LRVB portion of the
+# code, in the interest of keeping it simple. Can add later.
diff --git a/pymc_extras/inference/deterministic_advi/jax.py b/pymc_extras/inference/deterministic_advi/jax.py
new file mode 100644
index 00000000..1e9e6394
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/jax.py
@@ -0,0 +1,63 @@
+from typing import Callable, Dict, Tuple
+import numpy as np
+import jax.numpy as jnp
+from jax import jit, vmap, value_and_grad, jvp, grad
+from functools import partial
+
+from pymc_extras.inference.deterministic_advi.core import DADVIFuns
+from pymc_extras.inference.deterministic_advi.optimization import count_decorator
+
+
+@partial(jit, static_argnums=0)
+def hvp(f, primals, tangents):
+    # Taken (and slightly modified) from:
+    # https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html
+    return jvp(grad(f), (primals,), (tangents,))[1]
+
+
+@jit
+def _make_draws(z, mean, log_sd):
+
+    draw = z * jnp.exp(log_sd) + mean
+
+    return draw
+
+
+@jit
+def _calculate_entropy(log_sds):
+
+    return jnp.sum(log_sds)
+
+
+def build_dadvi_funs(log_posterior_fn: Callable[[jnp.ndarray], float]) -> DADVIFuns:
+    """
+    Builds the DADVIFuns from a log posterior density function written in JAX.
+    """
+
+    def single_log_posterior_fun(cur_z, var_params):
+        means, log_sds = jnp.split(var_params, 2)
+        cur_theta = _make_draws(cur_z, means, log_sds)
+        return log_posterior_fn(cur_theta)
+
+    def log_posterior_expectation(zs, var_params):
+        single_curried = partial(single_log_posterior_fun, var_params=var_params)
+        log_posts = vmap(single_curried)(zs)
+        return jnp.mean(log_posts)
+
+    def full_kl_est(var_params, zs):
+        _, log_sds = jnp.split(var_params, 2)
+        log_posterior = log_posterior_expectation(zs, var_params)
+        entropy = _calculate_entropy(log_sds)
+        return -log_posterior - entropy
+
+    @jit
+    def kl_est_hvp_fun(var_params, zs, b):
+        rel_kl_est = partial(full_kl_est, zs=zs)
+        rel_hvp = lambda x, y: hvp(rel_kl_est, x, y)
+        return rel_hvp(var_params, b)
+
+    kl_est_and_grad_fun = jit(value_and_grad(full_kl_est))
+
+    return DADVIFuns(
+        kl_est_and_grad_fun=kl_est_and_grad_fun, kl_est_hvp_fun=kl_est_hvp_fun
+    )
diff --git a/pymc_extras/inference/deterministic_advi/optimization.py b/pymc_extras/inference/deterministic_advi/optimization.py
new file mode 100644
index 00000000..8c243c9b
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/optimization.py
@@ -0,0 +1,90 @@
+import time
+import numpy as np
+from functools import wraps, partial
+from scipy.optimize import minimize
+
+
+def print_decorator(fun, verbose=True):
+    def result(x):
+
+        value, grad = fun(x)
+
+        if verbose:
+            print(f"'f': {value}, ||grad(f)||: {np.linalg.norm(grad)}", flush=True)
+
+        return value, grad
+
+    return result
+
+
+def count_decorator(function):
+    # If wrapped around a function, the number of calls of the function can be
+    # accessed by calling function.calls on the decorated result.
+    @wraps(function)
+    def new_fun(*args, **kwargs):
+        new_fun.calls += 1
+        return function(*args, **kwargs)
+
+    new_fun.calls = 0
+    return new_fun
+
+
+def time_decorator(function):
+    @wraps(function)
+    def new_fun(*args, **kwargs):
+        start_time = time.time()
+        result = function(*args, **kwargs)
+        end_time = time.time()
+        difference = end_time - start_time
+        new_fun.wall_time = difference
+        return result
+
+    new_fun.wall_time = None
+    return new_fun
+
+
+def optimize_with_hvp(
+    val_grad_fun,
+    hvp_fun,
+    start_params,
+    opt_method="trust-ncg",
+    verbose=False,
+    additional_decorator=None,
+    minimize_kwargs={},
+    callback_fun=None,
+):
+    # Note: "additional_decorator" will be called on the val_and_grad_fun and
+    # its purpose is to allow for additional side effects, particularly saving
+    # call results to files.
+
+    val_grad_fun = (
+        val_grad_fun
+        if additional_decorator is None
+        else additional_decorator(val_grad_fun)
+    )
+
+    decorated = count_decorator(partial(print_decorator, verbose=verbose)(val_grad_fun))
+    hvp_fun = count_decorator(hvp_fun)
+
+    if callback_fun is not None:
+        callback = lambda cur_theta: callback_fun(cur_theta, decorated, hvp_fun)
+    else:
+        callback = None
+
+    result = minimize(
+        decorated,
+        start_params,
+        method=opt_method,
+        hessp=hvp_fun,
+        jac=True,
+        callback=callback,
+        **minimize_kwargs,
+    )
+
+    n_hvp_calls = hvp_fun.calls
+    n_val_and_grad_calls = decorated.calls
+
+    return (
+        result,
+        {"n_hvp_calls": n_hvp_calls, "n_val_and_grad_calls": n_val_and_grad_calls},
+    )
diff --git a/pymc_extras/inference/deterministic_advi/pymc_to_jax.py b/pymc_extras/inference/deterministic_advi/pymc_to_jax.py
new file mode 100644
index 00000000..07438dc5
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/pymc_to_jax.py
@@ -0,0 +1,81 @@
+from jax.flatten_util import ravel_pytree
+from pymc.sampling.jax import get_jaxified_logp, get_jaxified_graph
+import jax
+from pymc.util import get_default_varnames
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+
+
+def get_logp_fn_dict(logp_fn, var_names):
+    def logp_fn_dict(theta_dict):
+        as_list = [theta_dict[x] for x in var_names]
+
+        return logp_fn(as_list)
+
+    return logp_fn_dict
+
+
+def get_basic_init_from_pymc(pymc_model):
+    logp_fn_jax = get_jaxified_logp(pymc_model)
+
+    rv_names = [rv.name for rv in pymc_model.value_vars]
+    init_point = pymc_model.initial_point()
+    init_state = {rv_name: init_point[rv_name] for rv_name in rv_names}
+
+    return rv_names, init_state, logp_fn_jax
+
+
+def get_jax_functions_from_pymc(pymc_model):
+    """
+    Given a PyMC model, builds functions for computing posterior densities with JAX.
+    Args:
+        pymc_model: The PyMC model object.
+    Returns:
+    A dictionary containing three elements: "log_posterior_fun" is the log posterior
+    density, as a function of a flat parameter vector; "unflatten_fun" turns a flat
+    parameter vector back into a dictionary; and "n_params" is the number of parameters
+    in the model.
+    """
+
+    var_names, init_state, logp_fn_jax = get_basic_init_from_pymc(pymc_model)
+    logp_fn_dict = get_logp_fn_dict(logp_fn_jax, var_names)
+    flat_init, fun = ravel_pytree(init_state)
+
+    def flat_log_post_fun(flat_params):
+        param_dict = fun(flat_params)
+        return logp_fn_dict(param_dict)
+
+    return {
+        "log_posterior_fun": flat_log_post_fun,
+        "unflatten_fun": fun,
+        "n_params": flat_init.shape[0],
+    }
+
+
+def transform_dadvi_draws(
+    pymc_model,
+    flat_dadvi_draws,
+    unflatten_fun,
+    keep_untransformed=False,
+    add_chain_dim=False,
+):
+    # TODO: Maybe should take unflattened draws as input instead
+
+    non_flat = jax.vmap(unflatten_fun)(flat_dadvi_draws)
+    list_version = [non_flat[x.name] for x in pymc_model.value_vars]
+
+    var_names = pymc_model.unobserved_value_vars
+
+    vars_to_sample = list(
+        get_default_varnames(var_names, include_transformed=keep_untransformed)
+    )
+
+    jax_fn = get_jaxified_graph(inputs=pymc_model.value_vars, outputs=vars_to_sample)
+
+    list_res = jax.vmap(jax_fn)(*list_version)
+    samples = {v.name: r for v, r in zip(vars_to_sample, list_res)}
+
+    if add_chain_dim:
+        samples = {x: np.expand_dims(y, axis=0) for x, y in samples.items()}
+
+    return samples
diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
new file mode 100644
index 00000000..98794ac3
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -0,0 +1,141 @@
+from collections import defaultdict
+
+import pymc
+import arviz as az
+import numpy as np
+from scipy.optimize import minimize
+import pytensor
+import pytensor.tensor as pt
+from pymc import join_nonshared_inputs, DictToArrayBijection
+from pymc.util import get_default_varnames
+
+from pymc_extras.inference.laplace_approx.scipy_interface import (
+    _compile_functions_for_scipy_optimize,
+)
+from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
+
+
+def create_dadvi_graph(
+    pymc_model, n_params: int, n_fixed_draws: int = 30, random_seed: int = 2
+):
+
+    state = np.random.RandomState(random_seed)
+
+    inputs = pymc_model.continuous_value_vars + pymc_model.discrete_value_vars
+    initial_point_dict = pymc_model.initial_point()
+    logp = pymc_model.logp()
+
+    # Graph in terms of a flat input
+    [logp], flat_input = join_nonshared_inputs(
+        point=initial_point_dict, outputs=[logp], inputs=inputs
+    )
+
+    draws = state.randn(n_fixed_draws, n_params)
+    var_params = pt.vector(name="eta", shape=(2 * n_params,))
+
+    means = var_params[:n_params]
+    log_sds = var_params[n_params:]
+
+    draw = pt.vector(name="draw", shape=(n_params,))
+    sample = means + pt.exp(log_sds) * draw
+
+    # Graph in terms of a single sample
+    logp_draw = pytensor.clone_replace(logp, replace={flat_input: sample})
+    draw_matrix = pt.constant(draws)
+
+    # Vectorise
+    logp_vectorized_draws = pytensor.graph.vectorize_graph(
+        logp_draw, replace={draw: draw_matrix}
+    )
+
+    mean_log_density = pt.mean(logp_vectorized_draws)
+    entropy = pt.sum(log_sds)
+
+    objective = -mean_log_density - entropy
+
+    return var_params, objective, n_params
+
+
+def transform_draws(unstacked_draws, model, n_draws, keep_untransformed=False):
+
+    filtered_var_names = model.unobserved_value_vars
+
+    vars_to_sample = list(
+        get_default_varnames(filtered_var_names, include_transformed=keep_untransformed)
+    )
+
+    fn = pytensor.function(model.value_vars, vars_to_sample)
+
+    d = {name: data.values for name, data in unstacked_draws.data_vars.items()}
+
+    transformed_draws = defaultdict(list)
+    vars_to_sample_names = [x.name for x in vars_to_sample]
+    raw_var_names = [x.name for x in model.value_vars]
+
+    for i in range(n_draws):
+
+        cur_draw = {x: y[0, i] for x, y in d.items()}
+        to_pass_in = [
+            cur_draw[cur_variable_name] for cur_variable_name in raw_var_names
+        ]
+        transformed = fn(*to_pass_in)
+
+        for cur_name, cur_value in zip(vars_to_sample_names, transformed):
+            transformed_draws[cur_name].append(cur_value)
+
+    final_dict = {
+        # Add a draw dimension
+        x: np.expand_dims(np.stack(y), axis=0)
+        for x, y in transformed_draws.items()
+    }
+
+    transformed_result = az.from_dict(posterior=final_dict)
+
+    return transformed_result
+
+
+def fit_deterministic_advi(
+    model=None,
+    n_fixed_draws: int = 30,
+    random_seed: int = 2,
+    n_draws: int = 1000,
+    keep_untransformed=False,
+):
+
+    model = pymc.modelcontext(model) if model is None else model
+
+    initial_point_dict = model.initial_point()
+    n_params = DictToArrayBijection.map(initial_point_dict).data.shape[0]
+
+    var_params, objective, n_params = create_dadvi_graph(
+        model,
+        n_fixed_draws=n_fixed_draws,
+        random_seed=random_seed,
+        n_params=n_params,
+    )
+
+    f_fused, f_hessp = _compile_functions_for_scipy_optimize(
+        objective,
+        [var_params],
+        compute_grad=True,
+        compute_hessp=True,
+        compute_hess=False,
+    )
+
+    result = minimize(
+        f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp
+    )
+
+    opt_var_params = result.x
+    opt_means, opt_log_sds = np.split(opt_var_params, 2)
+
+    # Make the draws:
+    draws_raw = np.random.randn(n_draws, n_params)
+    draws = opt_means + draws_raw * np.exp(opt_log_sds)
+    draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
+
+    transformed_draws = transform_draws(
+        draws_arviz, model, n_draws=n_draws, keep_untransformed=keep_untransformed
+    )
+
+    return transformed_draws
diff --git a/pymc_extras/inference/deterministic_advi/utils.py b/pymc_extras/inference/deterministic_advi/utils.py
new file mode 100644
index 00000000..c2caddf1
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/utils.py
@@ -0,0 +1,18 @@
+from time import time
+
+
+def opt_callback_fun(theta, val_and_grad_fun, hvp_fun):
+    # Callback for jax_advi. It records only what will be needed
+    # to estimate the KL later on.
+
+    opt_callback_fun.opt_sequence.append(
+        {
+            "val_and_grad_calls": val_and_grad_fun.calls,
+            "hvp_calls": hvp_fun.calls,
+            "theta": theta,
+            "time": time(),
+        }
+    )
+
+
+opt_callback_fun.opt_sequence = list()
diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index ac51e76b..390d2bc0 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -40,3 +40,13 @@ def fit(method: str, **kwargs) -> az.InferenceData:
         from pymc_extras.inference import fit_laplace
 
         return fit_laplace(**kwargs)
+
+    if method == "deterministic_advi":
+        from pymc_extras.inference import fit_deterministic_advi
+
+        return fit_deterministic_advi(**kwargs)
+
+    if method == "deterministic_advi_jax":
+        from pymc_extras.inference import fit_deterministic_advi_jax
+
+        return fit_deterministic_advi_jax(**kwargs)
diff --git a/pyproject.toml b/pyproject.toml
index c7bc7e32..963258f8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,6 +40,7 @@ dependencies = [
   "better-optimize>=0.1.5",
   "pydantic>=2.0.0",
   "preliz>=0.20.0",
+  "jax>=0.7.0"
 ]
 
 [project.optional-dependencies]