diff --git a/identify_linear_expression.ipynb b/identify_linear_expression.ipynb
new file mode 100644
index 0000000000..ba3aebfe35
--- /dev/null
+++ b/identify_linear_expression.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BK5As0cbUejz",
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "from etuples import etuple\n",
+    "from unification import unify, var\n",
+    "\n",
+    "import pytensor.tensor as pt\n",
+    "from pytensor.graph import rewrite_graph\n",
+    "from pytensor.graph.fg import FunctionGraph\n",
+    "from pytensor.graph.rewriting.basic import MergeOptimizer, PatternNodeRewriter, out2in"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.438328768Z",
+     "start_time": "2025-08-14T11:29:54.500174Z"
+    },
+    "id": "alNycwOIUzTM"
+   },
+   "outputs": [],
+   "source": [
+    "def find_optimal_P(P, Q, mc):\n",
+    "    pi = (Q * (P - mc)).sum()\n",
+    "    dpi_dP = pt.grad(pi, P)\n",
+    "    # P_star, success = root(dpi_dP, P, method=\"hybr\", optimizer_kwargs=dict(tol=1e-8))\n",
+    "    # return P_star, success\n",
+    "    return dpi_dP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.440094174Z",
+     "start_time": "2025-08-14T11:31:54.469010Z"
+    },
+    "id": "wVnYGz8GVKb4"
+   },
+   "outputs": [],
+   "source": [
+    "price_effect = pt.scalar(\"price_effect\")\n",
+    "price = pt.vector(\"price\")\n",
+    "trend = pt.vector(\"trend\")\n",
+    "seasonality = pt.vector(\"seasonality\")\n",
+    "mc = pt.scalar(\"marginal_cost\")\n",
+    "\n",
+    "price_term = price * price_effect\n",
+    "expected_sales = trend + price_term + seasonality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.440827348Z",
+     "start_time": "2025-08-14T11:31:54.681476Z"
+    },
+    "id": "BeitshYMVkQU"
+   },
+   "outputs": [],
+   "source": [
+    "expr = find_optimal_P(price, expected_sales, mc=mc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.443902007Z",
+     "start_time": "2025-08-14T11:31:54.918556Z"
+    },
+    "id": "jugOxL4DcRFN"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Add [id A] 5\n",
+      " ├─ Mul [id B] 4\n",
+      " │  ├─ Sub [id C] 3\n",
+      " │  │  ├─ price [id D]\n",
+      " │  │  └─ ExpandDims{axis=0} [id E] 2\n",
+      " │  │     └─ marginal_cost [id F]\n",
+      " │  └─ ExpandDims{axis=0} [id G] 0\n",
+      " │     └─ price_effect [id H]\n",
+      " ├─ trend [id I]\n",
+      " ├─ Mul [id J] 1\n",
+      " │  ├─ price [id D]\n",
+      " │  └─ ExpandDims{axis=0} [id G] 0\n",
+      " │     └─ ···\n",
+      " └─ seasonality [id K]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<ipykernel.iostream.OutStream at 0x7fcbccd613c0>"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use existing rewrites to simplify expression\n",
+    "fgraph = FunctionGraph(outputs=[expr], clone=False)\n",
+    "rewrite_graph(fgraph, include=(\"canonicalize\",))\n",
+    "fgraph.dprint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.445406846Z",
+     "start_time": "2025-08-14T11:31:55.243098Z"
+    },
+    "id": "86-KeCOFWQZU"
+   },
+   "outputs": [],
+   "source": [
+    "# distribute_mul_over_add = PatternNodeRewriter(\n",
+    "#     (pt.mul, (pt.add, \"x\", \"y\"), \"z\"),\n",
+    "#     (pt.add, (pt.mul, \"x\", \"z\"), (pt.mul, \"y\", \"z\")),\n",
+    "# )\n",
+    "\n",
+    "distribute_mul_over_sub = PatternNodeRewriter(\n",
+    "    (pt.mul, (pt.sub, \"x\", \"y\"), \"z\"),\n",
+    "    (pt.add, (pt.mul, \"x\", \"z\"), (pt.mul, (pt.neg, \"y\"), \"z\")),\n",
+    ")\n",
+    "\n",
+    "combine_addition_terms = PatternNodeRewriter(\n",
+    "    (pt.add, (pt.add, \"x\", \"y\"), \"z\", \"x\", \"w\"),\n",
+    "    (pt.add, (pt.mul, \"x\", 2), (pt.add, \"y\", \"z\", \"w\")),\n",
+    ")\n",
+    "\n",
+    "# distribute_mul_over_add = out2in(distribute_mul_over_add, name=\"distribute_mul_add\")\n",
+    "distribute_mul_over_sub = out2in(distribute_mul_over_sub, name=\"distribute_mul_sub\")\n",
+    "combine_addition_terms = out2in(combine_addition_terms, name=\"combine_addition_terms\")\n",
+    "\n",
+    "# distribute\n",
+    "distribute_mul_over_sub.rewrite(fgraph)\n",
+    "# merge equivalent terms\n",
+    "MergeOptimizer().rewrite(fgraph)\n",
+    "# combine equivalent terms\n",
+    "combine_addition_terms.rewrite(fgraph)\n",
+    "# extract rewritten expression\n",
+    "expr = fgraph.outputs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.446341276Z",
+     "start_time": "2025-08-14T11:31:56.276558Z"
+    },
+    "id": "4qGBap72Xvvn"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Add [id A]\n",
+      " ├─ Mul [id B]\n",
+      " │  ├─ Mul [id C]\n",
+      " │  │  ├─ price [id D]\n",
+      " │  │  └─ ExpandDims{axis=0} [id E]\n",
+      " │  │     └─ price_effect [id F]\n",
+      " │  └─ ExpandDims{axis=0} [id G]\n",
+      " │     └─ 2 [id H]\n",
+      " └─ Add [id I]\n",
+      "    ├─ Mul [id J]\n",
+      "    │  ├─ Neg [id K]\n",
+      "    │  │  └─ ExpandDims{axis=0} [id L]\n",
+      "    │  │     └─ marginal_cost [id M]\n",
+      "    │  └─ ExpandDims{axis=0} [id E]\n",
+      "    │     └─ ···\n",
+      "    ├─ trend [id N]\n",
+      "    └─ seasonality [id O]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<ipykernel.iostream.OutStream at 0x7fcbccd613c0>"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr.dprint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.447033733Z",
+     "start_time": "2025-08-14T11:31:59.481064Z"
+    },
+    "id": "8Fq10k2LcCY-"
+   },
+   "outputs": [],
+   "source": [
+    "# Create variations of a graph for pattern matching\n",
+    "rewrites = [\n",
+    "    out2in(\n",
+    "        PatternNodeRewriter((pt.add, \"x\", \"y\"), (pt.add, \"y\", \"x\")),\n",
+    "        name=\"commutative_add\",\n",
+    "        ignore_newtrees=True,\n",
+    "    ),\n",
+    "    out2in(\n",
+    "        PatternNodeRewriter((pt.mul, \"x\", \"y\"), (pt.mul, \"y\", \"x\")),\n",
+    "        name=\"commutative_mul\",\n",
+    "        ignore_newtrees=True,\n",
+    "    ),\n",
+    "    out2in(\n",
+    "        PatternNodeRewriter(\n",
+    "            (pt.mul, (pt.mul, \"x\", \"y\"), \"z\"), (pt.mul, \"x\", (pt.mul, \"y\", \"z\"))\n",
+    "        ),\n",
+    "        name=\"associative_mul\",\n",
+    "        ignore_newtrees=True,\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def yield_arithmetic_variants(expr, n):\n",
+    "    fgraph = FunctionGraph(outputs=[expr], clone=False)\n",
+    "    while n > 0:\n",
+    "        rewrite = random.choice(rewrites)\n",
+    "        res = rewrite.apply(fgraph)\n",
+    "        n -= 1\n",
+    "        if res:\n",
+    "            yield fgraph.outputs[0]\n",
+    "    yield fgraph.outputs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.447578804Z",
+     "start_time": "2025-08-14T11:31:59.831774Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 198
+    },
+    "id": "h9K70LGxYJ7E",
+    "outputId": "793e98c6-4570-43bf-a452-eb6d0d745dc7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{~price: price, ~a: Mul.0, ~b: Add.0}"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Rewrite graph randomly until we match price * a + b\n",
+    "a, b, price_ = var(\"a\"), var(\"b\"), var(\"price\")\n",
+    "pattern = etuple(pt.add, etuple(pt.mul, price_, a), b)\n",
+    "\n",
+    "for variant in yield_arithmetic_variants(expr, n=100):\n",
+    "    match_dict = unify(variant, pattern)\n",
+    "    if match_dict and match_dict[price_] is price:\n",
+    "        break\n",
+    "else:\n",
+    "    raise ValueError(\"No matching variant found\")\n",
+    "match_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.448905279Z",
+     "start_time": "2025-08-14T11:32:01.264784Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8M-qjXBKa6Db",
+    "outputId": "cdce40c4-e1dd-4757-f4d6-f368643bb5c1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True_div [id A]\n",
+      " ├─ Neg [id B]\n",
+      " │  └─ Add [id C]\n",
+      " │     ├─ Mul [id D]\n",
+      " │     │  ├─ Neg [id E]\n",
+      " │     │  │  └─ ExpandDims{axis=0} [id F]\n",
+      " │     │  │     └─ marginal_cost [id G]\n",
+      " │     │  └─ ExpandDims{axis=0} [id H]\n",
+      " │     │     └─ price_effect [id I]\n",
+      " │     ├─ trend [id J]\n",
+      " │     └─ seasonality [id K]\n",
+      " └─ Mul [id L]\n",
+      "    ├─ ExpandDims{axis=0} [id H]\n",
+      "    │  └─ ···\n",
+      "    └─ ExpandDims{axis=0} [id M]\n",
+      "       └─ 2 [id N]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<ipykernel.iostream.OutStream at 0x7fcbccd613c0>"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimal_result = -match_dict[b] / match_dict[a]\n",
+    "optimal_result.dprint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T11:32:09.449645675Z",
+     "start_time": "2025-08-14T11:25:52.269957Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pytensor/scalar/math.py b/pytensor/scalar/math.py
index d08759a978..28ca7486ca 100644
--- a/pytensor/scalar/math.py
+++ b/pytensor/scalar/math.py
@@ -1315,7 +1315,7 @@ def c_code_cache_version(self):
             return v
 
 
-softplus = Softplus(upgrade_to_float, name="scalar_softplus")
+softplus = Softplus(upgrade_to_float, name="softplus")
 
 
 class Log1mexp(UnaryScalarOp):
diff --git a/pytensor_tutorial_pricing.ipynb b/pytensor_tutorial_pricing.ipynb
new file mode 100644
index 0000000000..2c39c43ae9
--- /dev/null
+++ b/pytensor_tutorial_pricing.ipynb
@@ -0,0 +1,3159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "28e4e5d2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-18T20:46:58.416962Z",
+     "start_time": "2025-08-18T20:46:58.309266Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'width': 1400, 'height': 768, 'scroll': True}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from notebook.services.config import ConfigManager\n",
+    "\n",
+    "\n",
+    "cm = ConfigManager()\n",
+    "cm.update(\n",
+    "    \"livereveal\",\n",
+    "    {\n",
+    "        \"width\": 1400,\n",
+    "        \"height\": 768,\n",
+    "        \"scroll\": True,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9e2ce3b7",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from copy import deepcopy\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from etuples import etuple\n",
+    "from IPython.display import clear_output\n",
+    "from unification import unify, var\n",
+    "\n",
+    "import pytensor\n",
+    "import pytensor.tensor as pt\n",
+    "from pytensor.graph import rewrite_graph\n",
+    "from pytensor.graph.basic import explicit_graph_inputs\n",
+    "from pytensor.graph.features import History\n",
+    "from pytensor.graph.fg import FunctionGraph\n",
+    "from pytensor.graph.replace import graph_replace\n",
+    "from pytensor.graph.rewriting.basic import MergeOptimizer, PatternNodeRewriter, out2in\n",
+    "from pytensor.tensor.optimize import root\n",
+    "\n",
+    "\n",
+    "np.seterr(all=\"ignore\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a3340e9e",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "SEED = sum(map(ord, \"Pytensor at EuroScipy\"))\n",
+    "rng = np.random.default_rng(SEED)\n",
+    "\n",
+    "plt.rcParams.update(\n",
+    "    {\n",
+    "        \"figure.figsize\": (14, 4),\n",
+    "        \"figure.dpi\": 144,\n",
+    "        \"figure.constrained_layout.use\": True,\n",
+    "        \"axes.spines.top\": False,\n",
+    "        \"axes.spines.bottom\": True,\n",
+    "        \"axes.spines.left\": True,\n",
+    "        \"axes.spines.right\": False,\n",
+    "        \"axes.grid\": True,\n",
+    "        \"grid.linewidth\": 0.5,\n",
+    "        \"grid.linestyle\": \"--\",\n",
+    "        \"xtick.labelsize\": \"x-large\",\n",
+    "        \"ytick.labelsize\": \"x-large\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69a5a94e",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Recent Developments in Pytensor, the Successor Package to Theano\n",
+    "\n",
+    "### Theano is dead, long live Theano"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bc7a98d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# About Us\n",
+    "\n",
+    "- Jesse Grabowski\n",
+    "  - Github [@jessegrabowski](https://github.com/jessegrabowski), [LinkedIn](https://www.linkedin.com/in/jessegrabowski/)\n",
+    "  - PhD Candidate at Paris 1 - Pantheon Sorbonne\n",
+    "  - 目前居住在上海\n",
+    "  \n",
+    "- Ricardo Vieira\n",
+    "  - Github [@ricardoV94](https://github.com/ricardoV94)\n",
+    "  - Principal data scientist at PyMC Labs\n",
+    "  - Living 2599 km from Lisbon, 292 km from Krakow, 4779 from Djibouti\n",
+    "\n",
+    "- Both: Core developers of PyTensor and PyMC (and more)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb75e67f-f1b5-4d44-bda6-d020f7635aab",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# What was Theano?"
+   ]
+  },
+  {
+   "attachments": {
+    "before-img.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "10be13c2",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Popular perspective: One of the first widely used deep learning libraries (dating back to 2007). Superseded by PyTorch/Tensorflow/JAX\n",
+    "\n",
+    "\n",
+    "![before-img.png](attachment:before-img.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "2a37930c-c466-4196-9329-858b70b45cac.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6c72d325-445e-4b81-956b-5914347a6dd6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Technical perspective: A pythonic library for doing meta-computation, with many unique capabilities up to this day.\n",
+    "\n",
+    "![Screenshot From 2025-08-19 14-39-36.png](attachment:2a37930c-c466-4196-9329-858b70b45cac.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "after-img-small.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "de0a6098",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# What is Pytensor?\n",
+    "\n",
+    "\n",
+    "![after-img-small.jpg](attachment:after-img-small.jpg)\n",
+    "\n",
+    "A library to define, manipulate, and compile computational graphs\n",
+    "\n",
+    "Also: A fork of (a fork of) Theano\n",
+    "\n",
+    "https://github.com/pymc-devs/pytensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1a34afc",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Who is it for?\n",
+    "\n",
+    "- **Researchers** who work with numerical computation and want performance + programatic exploration of the problem-space.\n",
+    "\n",
+    "- **Developers** who want to offer expressive data-science frameworks, without pre-commiting to a specific computation library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ed3de9",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### What the user wants to write"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b7c60c8f",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.special import expit\n",
+    "\n",
+    "\n",
+    "def cross_entropy_loss(y, p):\n",
+    "    return (y * np.log(p) + (1 - y) * np.log(1 - p)).sum()\n",
+    "\n",
+    "\n",
+    "def compute_logistic_loss(X, y, alpha, beta):\n",
+    "    p = expit(alpha + X @ beta)\n",
+    "    return cross_entropy_loss(y, p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15f4b909",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### What the developer wants to read"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0d59bd3d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X_pt = pt.tensor(\"X\", shape=(None, 3))\n",
+    "y_pt = pt.tensor(\"y\", shape=(None,))\n",
+    "\n",
+    "alpha_pt = pt.tensor(\"alpha\", shape=())\n",
+    "beta_pt = pt.tensor(\"beta\", shape=(3,))\n",
+    "\n",
+    "p = pt.sigmoid(alpha_pt + X_pt @ beta_pt)\n",
+    "loss = (y_pt * pt.log(p) + (1 - y_pt) * pt.log(1 - p)).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6df0677f",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum{axes=None}\n",
+      " └─ Add\n",
+      "    ├─ Mul\n",
+      "    │  ├─ y\n",
+      "    │  └─ Log\n",
+      "    │     └─ Sigmoid\n",
+      "    │        └─ Add\n",
+      "    │           ├─ ExpandDims{axis=0}\n",
+      "    │           │  └─ alpha\n",
+      "    │           └─ Squeeze{axis=1}\n",
+      "    │              └─ Matmul\n",
+      "    │                 ├─ X\n",
+      "    │                 └─ ExpandDims{axis=1}\n",
+      "    │                    └─ beta\n",
+      "    └─ Mul\n",
+      "       ├─ Sub\n",
+      "       │  ├─ ExpandDims{axis=0}\n",
+      "       │  │  └─ 1\n",
+      "       │  └─ y\n",
+      "       └─ Log\n",
+      "          └─ Sub\n",
+      "             ├─ ExpandDims{axis=0}\n",
+      "             │  └─ 1\n",
+      "             └─ Sigmoid\n",
+      "                └─ ···\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<ipykernel.iostream.OutStream at 0x1239093f0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss.dprint(id_type=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "457080c7-d36c-409a-8a69-8bb62e02db49",
+   "metadata": {},
+   "source": [
+    "### What the developer gets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c8afd178-1dbe-45f4-8668-5a1be319964b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.compute_logistic_loss(X, y, alpha, beta)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compute_logistic_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "90771703-9dc4-43d2-903f-81f934ca11f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "??compute_logistic_loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e460ed-e4d4-4bcd-9d40-d5285a049c39",
+   "metadata": {},
+   "source": [
+    "* Text-parsing\n",
+    "* Byte-code parsing?\n",
+    "* Tracing?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cd9701f",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### The PyTensor solution: start with the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "53180b93",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pytensor.tensor as pt\n",
+    "\n",
+    "\n",
+    "X_pt = pt.tensor(\"X\", shape=(None, 3))\n",
+    "y_pt = pt.tensor(\"y\", shape=(None,))\n",
+    "alpha_pt = pt.tensor(\"alpha\", shape=())\n",
+    "beta_pt = pt.tensor(\"beta\", shape=(3,))\n",
+    "\n",
+    "p = pt.sigmoid(alpha_pt + X_pt @ beta_pt)\n",
+    "loss = (y_pt * pt.log(p) + (1 - y_pt) * pt.log(1 - p)).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c69a43f-e884-4728-b1d5-db3b6cd080b1",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Every variable is a symbolic place-holder, with a strict type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c81325a1-6a43-4b0f-8383-692d08fc13f3",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sum{axes=None}.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2c0a5be5591f936",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(pytensor.tensor.variable.TensorVariable, TensorType(float64, shape=()))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(loss), loss.type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8df944a9-cb6b-4790-a387-8a70ec61662e",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Variables can either be root inputs, or created by the application of operations, which connect inputs to outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bc75e500fb9ece2f",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pytensor.graph.basic.Apply"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(loss.owner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f1bb3439391f2509",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(pytensor.tensor.math.Sum, 'Sum{axes=None}')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(loss.owner.op), str(loss.owner.op)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "da1d7026-b462-4f6c-b8e4-fdf1f84cc155",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([Add.0], [Sum{axes=None}.0])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss.owner.inputs, loss.owner.outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c9a8edd7-f765-4118-bd00-400a6fe40437",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(pytensor.tensor.variable.TensorVariable, TensorType(float64, shape=(None,)))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(loss.owner.inputs[0]), loss.owner.inputs[0].type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b57e98dc",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "We'll stop here for now, but we could navigate the whole graph from outputs to inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "316c181a13627039",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Add\n",
+      " ├─ Mul\n",
+      " │  ├─ y\n",
+      " │  └─ Log\n",
+      " │     └─ Sigmoid\n",
+      " │        └─ Add\n",
+      " │           ├─ ExpandDims{axis=0}\n",
+      " │           │  └─ alpha\n",
+      " │           └─ Squeeze{axis=1}\n",
+      " │              └─ Matmul\n",
+      " │                 ├─ X\n",
+      " │                 └─ ExpandDims{axis=1}\n",
+      " │                    └─ beta\n",
+      " └─ Mul\n",
+      "    ├─ Sub\n",
+      "    │  ├─ ExpandDims{axis=0}\n",
+      "    │  │  └─ 1\n",
+      "    │  └─ y\n",
+      "    └─ Log\n",
+      "       └─ Sub\n",
+      "          ├─ ExpandDims{axis=0}\n",
+      "          │  └─ 1\n",
+      "          └─ Sigmoid\n",
+      "             └─ ···\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<ipykernel.iostream.OutStream at 0x1239093f0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss.owner.inputs[0].dprint(id_type=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a00bc280",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## What can you do with a graph?\n",
+    "\n",
+    "- Query it\n",
+    "- Transform it \n",
+    "- Evaluate it\n",
+    "- Rinse and repeat\n",
+    "\n",
+    "**... all in Python!**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75dceee0",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Query\n",
+    "\n",
+    "Find the inputs to the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d41f93fd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[y, alpha, X, beta]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(pytensor.graph.basic.explicit_graph_inputs(loss))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2bb5ca2",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Are there any patterns that we can take advantage of? \n",
+    "`log(sigmoid(x))` is an expression that shows often in our domain, and can be numerically optimized. Let's try and find it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5fd93260",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pytensor.graph.basic import ancestors\n",
+    "\n",
+    "\n",
+    "def find_log_sigmoid(variable):\n",
+    "    # Walk through the ancestors of a variable\n",
+    "    for var in ancestors([variable]):\n",
+    "        if (\n",
+    "            # Check it is not a root variable\n",
+    "            var.owner is not None\n",
+    "            # and comes out of the application of a log\n",
+    "            and var.owner.op == pt.log\n",
+    "            # whose input is also not a root variable\n",
+    "            and var.owner.inputs[0].owner is not None\n",
+    "            # and comes out of the application of a sigmoid\n",
+    "            and var.owner.inputs[0].owner.op == pt.sigmoid\n",
+    "        ):\n",
+    "            yield var"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bce490af",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Log.0]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(find_log_sigmoid(loss))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "916d7981",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Transform\n",
+    "\n",
+    "#### Stabilize\n",
+    "\n",
+    "Our `log(sigmoid(x))` can be written as `-softplus(-x)`, which turns out to be more stable.\n",
+    "\n",
+    "PyTensor allows to easily replace equivalent terms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "38e081a0",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "stable_loss = loss\n",
+    "for log_sigmoid_term in find_log_sigmoid(loss):\n",
+    "    x = log_sigmoid_term.owner.inputs[0].owner.inputs[0]\n",
+    "    stable_loss = graph_replace(\n",
+    "        stable_loss, replace={log_sigmoid_term: -pt.softplus(-x)}\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3f7e9ab",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "#### Add randomness to the system\n",
+    "\n",
+    "We can also mutate a graph to alter its behavior. Say to add randomness dowstream of some variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "685575d4",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "noisy_loss = graph_replace(loss, replace={X_pt: X_pt + pt.random.normal(0, 1)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6aa053d8",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Ablate part of the system\n",
+    "\n",
+    "Or ablate part of the graph, say by removing the contribution from the bias term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b9264d11",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ablated_loss = graph_replace(loss, replace={alpha_pt: alpha_pt * 0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "205b30e0",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Other common transformations\n",
+    "\n",
+    "- Autodiff\n",
+    "- Vectorization\n",
+    "- Bit quantization\n",
+    "- Backend specialization\n",
+    "- Numerical simplification\n",
+    "- Dead code elimination"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecb6b6d8",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Evaluate (performantly)\n",
+    "\n",
+    "At the end of the day we need to crunch real numbers. \n",
+    "\n",
+    "PyTensor allows compiling the same graph to different computational backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "56293fd6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "eval_dict = {\n",
+    "    X_pt: rng.normal(size=(100, 3)),\n",
+    "    y_pt: rng.binomial(n=1, p=0.2, size=(100,)),\n",
+    "    alpha_pt: rng.normal(size=()),\n",
+    "    beta_pt: rng.normal(size=(3,)),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b92143c6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(-86.58472878)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss.eval(eval_dict)  # By default it uses a custom C backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5d464526",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array(-86.58472878, dtype=float64)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stable_loss.eval(eval_dict, mode=\"JAX\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8be8abcd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(-96.51436343)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "noisy_loss.eval(eval_dict, mode=\"NUMBA\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "8fbd53c9",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(-129.37799432)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    import torch\n",
+    "\n",
+    "    mode = \"PYTORCH\"\n",
+    "except ModuleNotFoundError:\n",
+    "    mode = \"NUMBA\"\n",
+    "\n",
+    "ablated_loss.eval(eval_dict, mode=mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b42a83",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Example sandbox: Sales Forecasting\n",
+    "\n",
+    "To motivate the features of Pytensor, I am going to use a specific example centered around timeseries forecasting. \n",
+    "\n",
+    "If you are not interested in this topic, I don't blame you. But I hope to use the example to show:\n",
+    "\n",
+    "1. How pytensor helps researchers accelerate their workflow\n",
+    "2. How developers can build on to of Pytensor to make extremely flexible software"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11b0ee70",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## From the researcher perspective\n",
+    "\n",
+    "A researcher is going to have a specific model that she's interested in working with. Although there is just one model, it might need to be transformed into many different forms, to do different tasks. For example:\n",
+    "\n",
+    "- Pre-estimation checks (simulation, finding solutions)\n",
+    "- Estimation (\"taking the model to data\") \n",
+    "- Post-estimation (using the model to make optimal decisions, forecasting, prediction)\n",
+    "\n",
+    "The reseracher does *not* care about any of the details about how this happens. She wants to be able to define the function once, then have a nice API that allows all these things to happen.\n",
+    "\n",
+    "In addition, we're going to assume she's not an expert in numerical optimizaiton. She'll give her model in the form that is most natural to her, as a researcher in her domain, **not** in the form that is most computationally snappy or numerically stable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e99676be",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## From the developer perspective\n",
+    "\n",
+    "On the developer side, we assume that there is a general form for the model or class of models we're interesting in supporting. There might be a suite of tricks, simplifications, and stabilizations known by the literature that will allow for better performance. \n",
+    "\n",
+    "The developer wants to support the maximum number of models possible, but he also wants performant code. Pytensor allows him to define the general case, then add machinery to analyze it and look for special cases.\n",
+    "\n",
+    "We will see this when we turn out attention to \"post-estimation\". We imagine that our package is not simply for estimating sales, but then also using that model. In this example, we will think about using the model of sales to choose optimal prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0212e627",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## The Prophet Model\n",
+    "\n",
+    "The prophet model is a time series decomposition model proposed by facebook in ...\n",
+    "\n",
+    "Like many time series models, it seeks to decompose an observed signal into a level, trend, and seasonality, so that:\n",
+    "\n",
+    "$$ y_t = \\text{level}_t + \\text{trend}_t + \\text{seasonality}_t $$\n",
+    "\n",
+    "Unlike other time series models, though, it does this using linear features. So the whole thing collapses back to good old OLS, but with carefully chosen features. \n",
+    "\n",
+    "As a result, it is easy to extend to include additional components. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02ab0f1d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Modeling Sales\n",
+    "\n",
+    "Suppose we want to model sales of some widgets as a function of price, which we get to set. We observe the sales (with some noise), as well as the prices (noiselessly, since we set them). \n",
+    "\n",
+    "Maybe our widgets are in higher or lower demand at different times in the year, so there are annual up and down cycles in the data, regardless of the price. Finally, there are economic forces we can't control: the market for our widgets goes up and down of its own accord.\n",
+    "\n",
+    "So we can use the prophet model to describe sales, adding in a regression term for the effect of price on sales:\n",
+    "\n",
+    "$$ \n",
+    "\\text{sales}_t = \\text{level}_t + \\text{trend}_t + \\text{seasonality}_t + \\beta \\cdot \\text{price}_t \n",
+    "$$\n",
+    "\n",
+    "For details about this model, there was a very nice presentation by Matthijs Brouns implementing it in PyMC [here](https://www.youtube.com/watch?v=appLxcMLT9Y). For our purposes here, you just need to understand that the model is a linear regression model with some fancy transformations of the time variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c8e259e0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:06.239675Z",
+     "start_time": "2025-08-14T14:00:06.234183Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def create_piecewise_trend(t, t_max, n_changepoints):\n",
+    "    \"\"\"\n",
+    "    Create a piecewise linear trend with n changepoints.\n",
+    "    \"\"\"\n",
+    "    s = pt.linspace(0, t_max, n_changepoints + 2)[1:-1]\n",
+    "    A = (t[:, None] > s) * 1\n",
+    "\n",
+    "    return A, s\n",
+    "\n",
+    "\n",
+    "def create_fourier_features(t, n, p=365.25):\n",
+    "    \"\"\"\n",
+    "    Create seasonal patterns using n fourier basis functions with period p\n",
+    "    \"\"\"\n",
+    "    x = 2 * np.pi * (pt.arange(n) + 1) * t[:, None] / p\n",
+    "    return pt.concatenate((pt.cos(x), pt.sin(x)), axis=1)\n",
+    "\n",
+    "\n",
+    "def generate_features(t, t_max, n_changepoints=10, n_fourier=6, p=365.25):\n",
+    "    \"\"\"\n",
+    "    Generate peicewise trend matrices A and s, and seasonal pattern matrix X.\n",
+    "    \"\"\"\n",
+    "    A, s = create_piecewise_trend(t, t_max, n_changepoints)\n",
+    "    X = create_fourier_features(t, n_fourier, p)\n",
+    "\n",
+    "    return A, s, X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "657f33ae",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Define Symbolic Inputs\n",
+    "\n",
+    "Pytensor straddles the line between a symbolic algebra system like Maple or Sympy, and a array library like numpy. In general, we try to adhere very closely to numpy syntax. But like a symbolic algebra system, a pytensor program starts by declaring root variables.\n",
+    "\n",
+    "As symbols, these root variables can be freely manipulated. When the time comes to compile our program, these will have to be given as inputs by the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b8a3cb2d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "price, time = pt.vectors(\"price\", \"time\")\n",
+    "\n",
+    "initial_intercept = pt.scalar(\"initial_intercept\")\n",
+    "initial_slope = pt.scalar(\"initial_slope\")\n",
+    "trend_changes = pt.vector(\"trend_changes\")\n",
+    "seasonal_effect = pt.vector(\"seasonal_effect\")\n",
+    "price_effect = pt.scalar(\"price_effect\")\n",
+    "\n",
+    "# Gather everything together into a lists; this will be handy later\n",
+    "input_data = [time, price]\n",
+    "\n",
+    "params = [\n",
+    "    initial_intercept,\n",
+    "    initial_slope,\n",
+    "    trend_changes,\n",
+    "    seasonal_effect,\n",
+    "    price_effect,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8b58603",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Define the Prophet model\n",
+    "\n",
+    "All of this syntax should look just like numpy. If you hadn't already seen that the variables being manipulated are symbolic `vector` and `scalar` objects, you wouldn't know this isn't just ordinary computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8f2db2a2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:06.311319Z",
+     "start_time": "2025-08-14T14:00:06.286197Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "A, s, X = generate_features(time, time.max(), p=52, n_fourier=1)\n",
+    "\n",
+    "intercept = initial_intercept + ((-s * A) * trend_changes[None]).sum(axis=1)\n",
+    "slope = (initial_slope + (A * trend_changes[None]).sum(axis=1)) * time\n",
+    "trend = intercept + slope\n",
+    "price_term = price_effect * price\n",
+    "\n",
+    "seasonal_term = X @ seasonal_effect\n",
+    "\n",
+    "expected_sales = trend + seasonal_term + price_term"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45777d36",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Compile a function\n",
+    "\n",
+    "Once we are done manipulating, we compile a function to actually perform numerical computation. This is done using `pytensor.function`. The `function` function needs to know:\n",
+    "\n",
+    "- `inputs`: root variables, to be provided by the user at runtime\n",
+    "- `outputs`: the variable(s) to be computed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f42335af",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sales_fn = pytensor.function(inputs=[*input_data, *params], outputs=expected_sales)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6bcbe27",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Create a function to generate random data\n",
+    "\n",
+    "Next, we also compile a random function to generate observed data, including iid random noise.\n",
+    "\n",
+    "Handling random number generators in Pytensor is beyond the scope of this tutorial, so just accept that this cell does what I claim it does. The long and short of it is that Pytensor is purely function, and does not allow side effects in functions by default. This includes advancing a random state! To handle this, we offer a `RandomStream` object, that functions like `np.default_rng()`.\n",
+    "\n",
+    "To learn more, you can read [this tutorial](https://pytensor.readthedocs.io/en/latest/tutorial/prng.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5913cede",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:07.107408Z",
+     "start_time": "2025-08-14T14:00:06.336499Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pt_rng = pt.random.RandomStream(seed=SEED)\n",
+    "observation_noise = pt_rng.normal(scale=1, size=expected_sales.shape)\n",
+    "\n",
+    "observed_sales = expected_sales + observation_noise\n",
+    "data_fn = pytensor.function([*input_data, *params], observed_sales)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2943c54",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Generate Data\n",
+    "\n",
+    "Generate some data with known parameter values. Our job will be to recover these values from observed data using some curve fitting algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "cabf1861",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:07.115009Z",
+     "start_time": "2025-08-14T14:00:07.111620Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "true_values = {\n",
+    "    \"initial_intercept\": 10.0,\n",
+    "    \"initial_slope\": 0.01,\n",
+    "    \"trend_changes\": rng.normal(scale=1e-3, size=(10,)),\n",
+    "    \"seasonal_effect\": np.array([2.0, 1.0]),\n",
+    "    \"price_effect\": -0.6,\n",
+    "}\n",
+    "T = 52 * 5\n",
+    "time_value = np.arange(T)\n",
+    "obs_prices = rng.normal(loc=10, scale=1, size=(T,))\n",
+    "sales_idx = rng.choice(T, size=(25,), replace=False)\n",
+    "\n",
+    "for idx in sales_idx:\n",
+    "    obs_prices[idx : idx + 4] /= 2\n",
+    "prices_obs = np.pad(\n",
+    "    np.convolve(obs_prices, np.full(5, 0.95 ** (np.arange(5))), mode=\"valid\") / 5,\n",
+    "    (2, 2),\n",
+    "    mode=\"mean\",\n",
+    ")\n",
+    "\n",
+    "sales_obs = data_fn(time_value, obs_prices, **true_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "43d6bb58-234c-401d-b369-d398d5bf7d2a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6.2953841 , 7.73225729, 7.70092471, 6.71385719, 6.87541428])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_obs[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "18bf581e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:07.305417Z",
+     "start_time": "2025-08-14T14:00:07.160318Z"
+    },
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2016x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
+    "for axis, data, title in zip(\n",
+    "    fig.axes, [obs_prices, sales_obs], [\"Price\", \"Observed Sales\"]\n",
+    "):\n",
+    "    axis.plot(data, lw=2)\n",
+    "    axis.set_title(title, fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "214067be",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Loss Function and Minimization\n",
+    "\n",
+    "To recover the parameters that generated the data, we can choose the parameters of the model to minimize the mean squared error between the estimation of the model and the data.\n",
+    "\n",
+    "There are lots of choices for how to actually do the minimization. We will do gradient descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "11c9bec6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:07.317394Z",
+     "start_time": "2025-08-14T14:00:07.313602Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "learning_rate = pt.scalar(\"learning_rate\")\n",
+    "observed_sales = pt.vector(\"sales\")\n",
+    "\n",
+    "loss = ((expected_sales - observed_sales) ** 2).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "fc5dc4910f8954b9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:07.363965Z",
+     "start_time": "2025-08-14T14:00:07.360396Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def sgd_optimization(update_fn, init_params, n_iter=60_000):\n",
+    "    optim_params = deepcopy(init_params)\n",
+    "\n",
+    "    history = np.empty(n_iter)\n",
+    "    start_i = None\n",
+    "\n",
+    "    for i in range(n_iter):\n",
+    "        curr_loss, *curr_grads = update_fn(**optim_params)\n",
+    "        lr = max(1e-3 * 0.999**i, 5e-5)\n",
+    "\n",
+    "        for key, grad in zip(optim_params.keys(), curr_grads):\n",
+    "            optim_params[key] -= lr * 2 * grad / (np.linalg.norm(grad) + 1e-8)\n",
+    "\n",
+    "        history[i] = curr_loss\n",
+    "        if curr_loss < 5 and not start_i:\n",
+    "            start_i = i\n",
+    "\n",
+    "        if start_i and (i % 1000 == 0):\n",
+    "            clear_output(wait=True)\n",
+    "            plt.plot(np.arange(start_i, i), history[start_i:i])\n",
+    "            plt.show(block=False)\n",
+    "\n",
+    "    return optim_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "8521ad39",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def optimize(loss, params, init_params, n_iter=60_000):\n",
+    "    param_grads = pt.grad(loss, params)\n",
+    "    update_fn = pytensor.function([*params], [loss, *param_grads])\n",
+    "\n",
+    "    return sgd_optimization(update_fn, init_params, n_iter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2a9b395",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Insert data into the graph\n",
+    "\n",
+    "We're not actually going to vary the data from call to call, so there's no reason to have symbolic inputs for the data. Instead, we we can just directly insert the data we're going to use.\n",
+    "\n",
+    "This is efficient because it gives Pytensor a chance to do constant folding. If we have something like `2 * observed_sales` in the graph, we can just compute that new constant once at compile time, rather than every time we call the funtion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a3cf0a03d705ff3d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:07.413545Z",
+     "start_time": "2025-08-14T14:00:07.407860Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "loss_w_data = graph_replace(\n",
+    "    loss, {time: time_value, price: prices_obs, observed_sales: sales_obs}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca44785cc513b0ae",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2016x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "init_params = {\n",
+    "    k: np.random.normal(scale=0.1, size=np.asarray(v).shape)\n",
+    "    for k, v in true_values.items()\n",
+    "}\n",
+    "init_params[\"initial_intercept\"] = np.array(sales_obs[0])\n",
+    "init_params[\"initial_slope\"] = np.array(np.diff(sales_obs)[1])\n",
+    "\n",
+    "optim_params = optimize(loss_w_data, params, init_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9a3d241",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "optim_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34803ec5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44098dc2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:14.047389Z",
+     "start_time": "2025-08-14T14:00:14.044521Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "predicted_sales = sales_fn(time_value, prices_obs, **optim_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f7f5221-211a-43e0-b2bd-5a4f3dd87d30",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "predicted_sales[:7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e97d9125",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:14.287952Z",
+     "start_time": "2025-08-14T14:00:14.100195Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(sales_obs, c=\"k\", label=\"Observed\", ls=\"--\")\n",
+    "plt.plot(predicted_sales, c=\"firebrick\", lw=2, label=\"Predicted\")\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33f2925b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Reusing Intermediate Results\n",
+    "\n",
+    "A simple place where the symbolic approach shines is when we are interested in intermediate computations.\n",
+    "\n",
+    "Specifically, compiling a function has no implications for any of the other symbolic variables we created along the way. So we are free to go back and continue manipulating, or even to compile new functions using differen parts of the graph.\n",
+    "\n",
+    "In the context of time series, we are often interesting in **time series decomposition**. That is, we want to know what part of the variance is attributable to the trend or the seasonality, and what is residual variance. \n",
+    "\n",
+    "In our case, we can simply ask for a new function that computes each of the intermediate bits that went into `expected_price`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "606bee0c",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Time Series Decomposition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63739e3f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:14.620862Z",
+     "start_time": "2025-08-14T14:00:14.351628Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "residual = observed_sales - expected_sales\n",
+    "f_decompose = pytensor.function(\n",
+    "    [time, price, observed_sales, *params],\n",
+    "    [intercept, slope, trend, seasonal_term, price_term, residual],\n",
+    ")\n",
+    "components = f_decompose(time_value, prices_obs, sales_obs, **optim_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21c276df",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:14.631568Z",
+     "start_time": "2025-08-14T14:00:14.629357Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "component_names = [\n",
+    "    \"Intercept\",\n",
+    "    \"Slope\",\n",
+    "    \"Trend = Intercept + Slope\",\n",
+    "    \"Seasonal Effect\",\n",
+    "    \"Price Effect\",\n",
+    "    \"Residual\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27503e5d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:14.988487Z",
+     "start_time": "2025-08-14T14:00:14.679424Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(6, 1, figsize=(14, 12), dpi=144, sharex=True)\n",
+    "for axis, data, name in zip(fig.axes, components, component_names):\n",
+    "    axis.plot(data)\n",
+    "    axis.set_title(name, fontdict={\"weight\": \"bold\"})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c8ebb53",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Graph Rewriting\n",
+    "\n",
+    "Although we introduced pytensor as a \"static graph\" library, the graphs generated by pytensor can be freely manipulated. In particular, we are allowed to replace variables -- or even entire subgraphs! -- with new variables or subgraphs.\n",
+    "\n",
+    "In the next example we modify the expected sales function to simulate a counterfactual scenario where the seasonality term is only 10% as strong during a specific period. Perhaps a new pandemic outbreak forced everyone indoors, and usual ciycles are disrupted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fac9905-c26e-4c47-81db-5ed23736a71b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dampened_seasonal = seasonal_term[150:200].set(seasonal_term[150:200] * 0.1)\n",
+    "expected_sales_dampened = graph_replace(\n",
+    "    expected_sales, {seasonal_term: dampened_seasonal}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35605033-83c7-4895-8ffb-a29bc8533ec5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sales_fn_dampened = pytensor.function(\n",
+    "    inputs=[*input_data, *params], outputs=expected_sales_dampened\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f12e2a84-6f8f-449e-8473-4b1c4d3beb6a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_predicted_sales = sales_fn_dampened(time_value, prices_obs, **optim_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c9bdd89-78b9-4de7-94b9-879420fe6a9b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(sales_obs, c=\"0.8\", label=\"Observed\", ls=\"-\", lw=3)\n",
+    "plt.plot(predicted_sales, c=\"firebrick\", lw=3, label=\"Predicted\")\n",
+    "plt.plot(new_predicted_sales, c=\"dodgerblue\", lw=3, ls=\"--\", label=\"Counterfactual\")\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e58a787",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## \"Constrained\" Optimization\n",
+    "\n",
+    "Often, we might want to restrict a parameter value to be in a certain range. For example, theory says the price effect should be negative -- if prices go up, demand should go down. To enfore this, we can use constrained optimization.\n",
+    "\n",
+    "One way to do this is to use an optimization algorithm that can handle boundaries. This would probably work fine in our case, but it can be fussy and we would have to import `scipy` and all that. \n",
+    "\n",
+    "Instead, we could also use a change of variables. Rather than choosing `price_effect`, we can have the optimizer choose `log(price_effect)`, then replace `price_effect` with `-exp(log(price_effect))` in the objective function. This means the optimizer can choose whatever it wants (because log(price_effect) can be any value in $\\mathbb R$), but `-exp(log(price_effect))` will be strictly negative when actually evaluating the loss.\n",
+    "\n",
+    "We will have to account for the presence of the `exp` when taking gradients, but autodiff will handle that for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dda9b05b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:15.016777Z",
+     "start_time": "2025-08-14T14:00:15.013174Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "log_price_effect = pt.scalar(\"log_price_effect\")\n",
+    "constrained_loss = graph_replace(loss_w_data, {price_effect: -pt.exp(log_price_effect)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afedc0b1",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_params = [*params[:-1], log_price_effect]\n",
+    "\n",
+    "new_init_params = deepcopy(init_params)\n",
+    "new_init_params[\"log_price_effect\"] = init_params[\"price_effect\"]\n",
+    "del new_init_params[\"price_effect\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "750738ab",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "constrained_optim_params = optimize(constrained_loss, new_params, new_init_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d2abb11",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.020130Z",
+     "start_time": "2025-08-14T14:00:22.017505Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "constrained_optim_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70493621",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Converting the unconstrained value back to the constrained space, we see that we get an answer similar to what we got when we did things the \"normal\" way.\n",
+    "\n",
+    "The point here was how easy it is to insert the unconstrained variable together with its inverse transformation in the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8d20f9f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.073622Z",
+     "start_time": "2025-08-14T14:00:22.071148Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "-np.exp(constrained_optim_params[\"log_price_effect\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b66e8495",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Post Estimation: Choosing Optimal Price\n",
+    "\n",
+    "We now have a demand function, which tells us how many widgets we will sell given a price. We can now ask how to set the price to maximimze total profits, given this demand function.\n",
+    "\n",
+    "Our profit is just what we make by selling $Q_t$ widgets, minus the cost it took us to \"produce\" those widgets. Don't take \"produce\" too seriously -- we might be reselling things, in which case the cost of \"production\" is the wholesale cost.  \n",
+    "\n",
+    "$$\n",
+    "\\pi_t = Q_t P_t - Q_t \\text{mc}_t = Q_t(P_t - \\text{mc}_t)\n",
+    "$$\n",
+    "\n",
+    "Where $Q_t$ is the quantity sold, $P_t$ is the unit price, and $\\text{mc}_t$ is the marginal cost of production for a single unit.\n",
+    "\n",
+    "We seek $P^\\star_t$ such that profits are maximized:\n",
+    "\n",
+    "$$\n",
+    "\\max_{P^\\star_t} Q_t(P^\\star_t - \\text{mc}_t)\n",
+    "$$\n",
+    "\n",
+    "The general strategy for solving these is that we take derivaties of the objective function with respect to the controls and set it equal to zero. In this case, thats $\\frac{\\partial \\pi_t}{\\partial P_t} = 0$. We then seek $P_t^\\star$ such that this equation is true.\n",
+    "\n",
+    "Assume we're making a package for this, and we know nothing about the $Q_t$ or $\\text{mc}_t$ that a user might provide. We can still symbolically solve this equation in the general case using a root finder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0403156",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.124362Z",
+     "start_time": "2025-08-14T14:00:22.122203Z"
+    },
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def find_optimal_P(P, Q, mc):\n",
+    "    pi = (Q * (P - mc)).sum()\n",
+    "    dpi_dP = pt.grad(pi, P)\n",
+    "    P_star, success = root(dpi_dP, P, method=\"lm\", optimizer_kwargs=dict(tol=1e-8))\n",
+    "    return P_star, success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99c8f5a5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.385504Z",
+     "start_time": "2025-08-14T14:00:22.174429Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Assume constant marginal cost\n",
+    "mc = pt.scalar(\"marginal_cost\")\n",
+    "\n",
+    "optimal_P, success = find_optimal_P(price, expected_sales, mc)\n",
+    "P_star_fn = pytensor.function([time, price, *params, mc], [optimal_P, success])\n",
+    "profit_fn = pytensor.function(\n",
+    "    [price, observed_sales, mc], observed_sales * (price - mc)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d865efd0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.455209Z",
+     "start_time": "2025-08-14T14:00:22.395131Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "P_star, success_flag = P_star_fn(\n",
+    "    time_value, np.zeros_like(time_value), **optim_params, marginal_cost=5.0\n",
+    ")\n",
+    "success_flag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b64e9e5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Optimal Price\n",
+    "\n",
+    "We can see that the optimal price fluctuates with demand. When demand is naturally higher, we should raise prices, and vice-versa. In general, we've been charging too little for the product."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c57d3f4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.586735Z",
+     "start_time": "2025-08-14T14:00:22.462889Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(P_star, lw=2, label=\"Optimal price\")\n",
+    "plt.plot(prices_obs, lw=2, label=\"Observed price\")\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09a04631",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Optimal Sales\n",
+    "\n",
+    "Plugging $P^\\star$ back into the demand function, we see that we've also been selling too much stuff."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36a1a29cb0b4ef4c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.717649Z",
+     "start_time": "2025-08-14T14:00:22.599125Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Q_star = sales_fn(time_value, P_star, **optim_params)\n",
+    "\n",
+    "plt.plot(Q_star, lw=2, label=\"Sales under optimal price\")\n",
+    "plt.plot(sales_obs, lw=2, label=\"Observed sales\")\n",
+    "\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a29de953",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Optimal Profits\n",
+    "\n",
+    "Given that we solved a maximization problem, we expect to the blue curve to be equal to or larger than the orange one. We see that that this is indeed the case. In a few periods we happened to be close to to the correct price by coincidence, but in general we weren't making as much as we could have been."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f3d7aaf33cffaf4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.841860Z",
+     "start_time": "2025-08-14T14:00:22.727940Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(\n",
+    "    profit_fn(P_star, Q_star, marginal_cost=5.0),\n",
+    "    lw=2,\n",
+    "    label=\"Profits under optimal price\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    profit_fn(\n",
+    "        prices_obs, sales_fn(time_value, prices_obs, **optim_params), marginal_cost=5.0\n",
+    "    ),\n",
+    "    lw=2,\n",
+    "    label=\"Observed profits\",\n",
+    ")\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02cf7ad4",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Supporting Special Cases: Linear Demand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bda8dae",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "If you are familiar with numerical optimizers, you know they can be fussy. We'd really like to avoid them if we can. In this case we can, because the demand function is just linear. Bascially, we have:\n",
+    "\n",
+    "$$ Q_t =f(t) + \\beta P_t $$\n",
+    "\n",
+    "Where $f(t) = \\text{level}_t + \\text{trend}_t + \\text{seasonality}_t$\n",
+    "\n",
+    "Substituting that into the profit function:\n",
+    "\n",
+    "$$\n",
+    "\\max_{P_t} \\pi_t = (f(t) + \\beta P_t) (P_t - \\text{mc}_t)\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "Expand terms:\n",
+    "\n",
+    "$$\n",
+    "\\max_{P_t} \\pi_t = f(t)P_t - f(t) \\text{mc}_t + \\beta P_t^2 - \\beta \\text{mc}_t P_t \n",
+    "$$\n",
+    "\n",
+    "Solve for a first-order condition:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\frac{\\partial \\pi_t}{\\partial P_t} &= 0 \\Rightarrow  \\\\\n",
+    "f(t) + 2 \\beta P_t - \\beta \\text{mc}_t &= 0 \\\\\n",
+    "P_t^\\star &= \\frac{\\beta \\text{mc}_t - f(t)}{2\\beta}\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This is a well-known result from Economics 101, giving the optimal price of a monopoly firm that faces a linear demand function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfc5787a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Graph Analysis\n",
+    "\n",
+    "To do this, we need to dig a bit deeper into how Pytensor works.\n",
+    "\n",
+    "First, every value returned by a pytensor function is itself a *computational graph*. At any point, we can stop and start manipulating that graph.\n",
+    "\n",
+    "In this case, the graph we are interested in working with is the derivative of profit with respect to price. We want to identify cases where this derivative is of the form $a + bP = 0$. Then, we can extract $a$ and $b$, and compute $P^\\star = -\\frac{a}{b}$\n",
+    "\n",
+    "This can be complex, though. $a$ and $b$ might themselves be complicated expressions. There are lots of weird little corner cases. What if $b$ is positive, but enters with subtraction, so we have $a - bP$? This is also valid, but we might miss it.\n",
+    "\n",
+    "To handle this, Pytensor has a database of transformations called **canonicalization**. This transforms a graph into a \"standard form\", so that further rewrites can reason about things in a standard way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c057bb00",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.944364Z",
+     "start_time": "2025-08-14T14:00:22.902639Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pi = (expected_sales * (price - mc)).sum()\n",
+    "dpi_dP = pt.grad(pi, price)\n",
+    "expr = rewrite_graph(dpi_dP, include=(\"canonicalize\",))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1409885",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Here is a string representation of the derivative, after canonicalization. \n",
+    "\n",
+    "We can see that it's linear in price, because the whole thing is just an addition (the outer-most node is an `Add` Op with several inputs). `price [id D]` appears twice, in the first term `Mul(Sub(price, marginal_cost), price_effect))` $\\rightarrow \\beta(P - \\text{mc})$, then in the last term, as `Mul(price_effect, price)` $\\rightarrow \\beta P$\n",
+    "\n",
+    "So we just need to do some algebra to get this into our $a + bP$ form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5cc257a302d8f6b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:22.957135Z",
+     "start_time": "2025-08-14T14:00:22.952165Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "expr.dprint(depth=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3090d8ab",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Recall that we are starting from the form $Q(P - mc)$, where $Q$ itself is a function of $P$. To get to our $a + bP$ form, we need to distribute the multiplication across everything. This is additional canonicalization that is not done by default.\n",
+    "\n",
+    "We can add it using `PatternNodeRewriter`. This works by pattern matching on a graph. Take the `distribution_mul_over_add` rewrite for example:\n",
+    "\n",
+    "```py\n",
+    "distribute_mul_over_add = PatternNodeRewriter(\n",
+    "    (pt.mul, (pt.add, \"x\", \"y\"), \"z\"),\n",
+    "    (pt.add, (pt.mul, \"z\", \"x\"), (pt.mul, \"z\", \"y\")),\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "It is going to look for any any expression of the form `(x + y) * z`, and replace it with `(xz + yz)`. The rewrite is written as a tuple-encoded abstract syntax tree in prefix notation, where each tuple first specifies an operator, then a list of inputs. \n",
+    "\n",
+    "- The first argument is what to look for: a multiplication with two inputs, where the first input is an additon with two inputs\n",
+    "- The second argumnet is what to to insert: an addition with two inputs, where each input is a multiplication of two inputs.\n",
+    "\n",
+    "The letters in the inputs and the outputs are matched and used consistently. So the first input to the input addition ends up as the 2nd input to the first multiplication in the output. \n",
+    "\n",
+    "We have to handle distribution over addition and subtraction separately, since these are different `Ops`. We also need a rewrite to collect repeated terms in an addition and replace them with multiplication. `combine_addition_terms` will look for expressions of the form `(x + y) + z + w + q + r + x` and replace it with `2x + y + z + w + q + r`.\n",
+    "\n",
+    "**Note!** If all this seems overfit to the specific problem, it is! `PatternNodeRewriter` is powerful, but also limited. In general, we can write rewrite *functions* to inspect and reason about graphs in arbitrary ways, all in pure python. For the purposes of this tutorial, however, we want to keep things simple, so we only use pattern rewrites. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a5a2215",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "distribute_mul_over_sub = PatternNodeRewriter(\n",
+    "    (pt.mul, (pt.sub, \"x\", \"y\"), \"z\"),\n",
+    "    (pt.add, (pt.mul, \"z\", \"x\"), (pt.mul, \"z\", (pt.neg, \"y\"))),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "762f27d7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:23.063446Z",
+     "start_time": "2025-08-14T14:00:23.053053Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "combine_addition_terms = PatternNodeRewriter(\n",
+    "    (pt.add, (pt.add, \"x\", \"y\"), \"z\", \"w\", \"q\", \"r\", \"x\"),\n",
+    "    (pt.add, (pt.mul, \"x\", 2), (pt.add, \"y\", \"z\", \"w\", \"q\", \"r\")),\n",
+    ")\n",
+    "\n",
+    "distribute_mul_over_sub = out2in(distribute_mul_over_sub, name=\"distribute_mul_sub\")\n",
+    "combine_addition_terms = out2in(combine_addition_terms, name=\"combine_addition_terms\")\n",
+    "\n",
+    "fgraph = FunctionGraph(outputs=[expr], clone=False)\n",
+    "\n",
+    "# Distribute multiplication\n",
+    "distribute_mul_over_sub.rewrite(fgraph)\n",
+    "\n",
+    "# Merge equivalent sub-expressions\n",
+    "MergeOptimizer().rewrite(fgraph)\n",
+    "\n",
+    "# Gather repeated additions into multiplication\n",
+    "combine_addition_terms.rewrite(fgraph)\n",
+    "\n",
+    "# Extract the rewritten expression\n",
+    "expr = fgraph.outputs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad906033",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:23.110862Z",
+     "start_time": "2025-08-14T14:00:23.106096Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "expr.dprint(depth=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9215219a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Then next step is to handle those pesky corner cases I mentioned. We want to make sure that we recognize $a + bP$, but also $bP + a$, or $Pb + a$, or $a + Pb$. \n",
+    "\n",
+    "The next 3 rewrites are just allowing inputs to be permuted. We know what we are looking for, and we can apply these rewrites in different combinations to do an exhaustive search. That way we can be sure we won't miss anything "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "742b67c8b729da37",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:23.211792Z",
+     "start_time": "2025-08-14T14:00:23.209182Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create variations of a graph for pattern matching\n",
+    "rewrites = [\n",
+    "    PatternNodeRewriter((pt.add, \"x\", \"y\"), (pt.add, \"y\", \"x\")),\n",
+    "    PatternNodeRewriter((pt.mul, \"x\", \"y\"), (pt.mul, \"y\", \"x\")),\n",
+    "    PatternNodeRewriter(\n",
+    "        (pt.mul, (pt.mul, \"x\", \"y\"), \"z\"), (pt.mul, \"x\", (pt.mul, \"y\", \"z\"))\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cb4048c",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `yield_rewrite_variants` function is very deep into the Pytensor weeds. \n",
+    "\n",
+    "It is going to apply the 3 associative rewrites defined in the `rewrites` dictionary in different combinations.\n",
+    "\n",
+    "If it fails, it is able to rewind the rewrite it tried, so we can try a new form.\n",
+    "\n",
+    "It will also hash each unique forms of the graph that it has tried, so that we don't end up stuck in an endless loop of meaningless permutations.\n",
+    "\n",
+    "For purposes here, it is not necessary that you totally understand the implementation details of what is happening here. Only the high-level concept. We seek $a + bP$, but it might be hidden in some equivalent form, so we need to do an exhaustive search across those equivalent forms.\n",
+    "\n",
+    "As a note, Pytensor supports relational programming using Kanren and Egraphs to more elegantly attack problems like this. We just don't want to open up that whole kettle of relational programming fish in this tutorial. For details, you can see a brief tutorial [here](https://pytensor.readthedocs.io/en/latest/extending/graph_rewriting.html#minikanren), or a conversation between Ricardo and one of the Egglog developers [here](https://egglog-python.readthedocs.io/latest/explanation/2023_11_17_pytensor.html) about using eGraphs in Pytensor.\n",
+    "\n",
+    "But this is also an area where there is a lot of room for active development. That is to say, PRs welcome :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31d3de4ff1ec52e5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:23.163429Z",
+     "start_time": "2025-08-14T14:00:23.159599Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def yield_rewrite_variants(expr, rewrites, variants_seen=None):\n",
+    "    yield expr\n",
+    "    if variants_seen is None:\n",
+    "        variants_seen = set()\n",
+    "    # HASH WITH text repr: genius!!!\n",
+    "    variants_seen.add(expr.dprint(file=\"str\"))\n",
+    "\n",
+    "    history = History()\n",
+    "    fgraph = FunctionGraph(outputs=[expr], clone=False)\n",
+    "    fgraph.attach_feature(history)\n",
+    "    toposort = fgraph.toposort()\n",
+    "\n",
+    "    for i, node in enumerate(reversed(toposort)):\n",
+    "        if len(node.outputs) > 1:\n",
+    "            # Only work with single output nodes\n",
+    "            continue\n",
+    "\n",
+    "        replacements = [rewrite.transform(fgraph, node) for rewrite in rewrites]\n",
+    "        for replacement in replacements:\n",
+    "            if not replacement:\n",
+    "                continue\n",
+    "            if isinstance(replacement, dict):\n",
+    "                raise ValueError(\"Dict replacement not supported\")\n",
+    "\n",
+    "            # Apply one of the replacements at a time, and recurse from there\n",
+    "            checkpoint = fgraph.checkpoint()\n",
+    "            fgraph.replace_all(tuple(zip(node.outputs, replacement, strict=True)))\n",
+    "            expr = fgraph.outputs[0]\n",
+    "            if expr.dprint(file=\"str\") not in variants_seen:\n",
+    "                # Try variants on top of this rewrite recursively\n",
+    "                yield from yield_rewrite_variants(\n",
+    "                    expr, rewrites, variants_seen=variants_seen\n",
+    "                )\n",
+    "            fgraph.revert(checkpoint)  # Go back and try the next branch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d94810c",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Finally, we're ready to go look for our $a + bP$ form. For this, we use an `etuple` together with `unify`. Details about what this all means can be found [here](https://pytensor.readthedocs.io/en/latest/extending/graph_rewriting.html#unification-and-reification).\n",
+    "\n",
+    "The short explaination is that we're using then `unification` package to perform [logical unification](https://en.wikipedia.org/wiki/Unification_(computer_science)). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb9e785fa8d12230",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:23.301205Z",
+     "start_time": "2025-08-14T14:00:23.259125Z"
+    },
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a, b, price_ = var(\"a\"), var(\"b\"), var(\"price\")\n",
+    "pattern = etuple(pt.add, etuple(pt.mul, price_, b), a)\n",
+    "\n",
+    "for variant in yield_rewrite_variants(expr, rewrites):\n",
+    "    match_dict = unify(variant, pattern)\n",
+    "    if match_dict and match_dict[price_] is price:\n",
+    "        break\n",
+    "else:\n",
+    "    raise ValueError(\"No matching variant found\")\n",
+    "match_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6569d1d2",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "We found a match, which returns the graphs `a`, `b`, and `P` in our target expression $a + bP$.\n",
+    "\n",
+    "As expected, $a$ is a big function of all the non-price terms from the Prophet model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d677b5f",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "match_dict[a].dprint(depth=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e3b1cf5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While $b = 2\\beta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "996ac546",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "match_dict[b].dprint()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcc50d0c",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "With this in hand, we symbolically compute $\\text{Optimal P} = -\\frac{a}{b}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be105626",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T14:00:23.337512Z",
+     "start_time": "2025-08-14T14:00:23.332030Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "symbolic_P_star = -match_dict[a] / match_dict[b]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b64c65",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fn_P_star_2 = pytensor.function(\n",
+    "    [time, price, *params, mc], symbolic_P_star, on_unused_input=\"ignore\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e0d50dd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "analytic_P_star = fn_P_star_2(time_value, prices_obs, **optim_params, marginal_cost=5.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e510ae79",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "As a sanity check, we plot our analytical solution found via rewrites against the numerical solution. As expected, they match!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2372f497",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(analytic_P_star, lw=3, label=\"Analytic Solution\")\n",
+    "plt.plot(P_star, ls=\"--\", lw=3, label=\"Numerical Solution\")\n",
+    "plt.legend(fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "577e11a9",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "# Repackage it nicely\n",
+    "\n",
+    "Of course, if this were a real API, we wouldn't want to expose any of this to the user. She should just give us a demand funciton, then we do the best we can with it.\n",
+    "\n",
+    "In this case, we want to first look for a linear solution. If we find one, great! We return that. Otherwise, we drop back to the numerical solver. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04580f82",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def cannonicalize_sales_expr(expr):\n",
+    "    fgraph = FunctionGraph(outputs=[expr], clone=False)\n",
+    "\n",
+    "    distribute_mul_over_sub.rewrite(fgraph)\n",
+    "    MergeOptimizer().rewrite(fgraph)\n",
+    "    combine_addition_terms.rewrite(fgraph)\n",
+    "    return fgraph.outputs[0]\n",
+    "\n",
+    "\n",
+    "def find_exact_linear_solution(expr):\n",
+    "    P_star, success = None, None\n",
+    "    expr = cannonicalize_sales_expr(expr)\n",
+    "\n",
+    "    a, b, price_ = var(\"a\"), var(\"b\"), var(\"price\")\n",
+    "    pattern = etuple(pt.add, etuple(pt.mul, price_, a), b)\n",
+    "\n",
+    "    for variant in yield_rewrite_variants(expr, rewrites):\n",
+    "        match_dict = unify(variant, pattern)\n",
+    "        if match_dict and match_dict[price_] is price:\n",
+    "            print(\"Found linear price function, using exact solution!\")\n",
+    "            P_star = -match_dict[b] / match_dict[a]\n",
+    "            success = pt.as_tensor(np.array(True))\n",
+    "            break\n",
+    "\n",
+    "    return P_star, success\n",
+    "\n",
+    "\n",
+    "def find_optimal_P_v2(P, Q, mc):\n",
+    "    pi = (Q * (P - mc)).sum()\n",
+    "    dpi_dP = pt.grad(pi, P)\n",
+    "\n",
+    "    expr = rewrite_graph(dpi_dP, include=(\"canonicalize\",))\n",
+    "\n",
+    "    # Try for the exact solution\n",
+    "    P_star, success = find_exact_linear_solution(expr)\n",
+    "\n",
+    "    # If we fail, fall back to a numerical optimizer\n",
+    "    if P_star is None:\n",
+    "        print(\"No exact solution avaiable, using numerical solver\")\n",
+    "        P_star, success = root(expr, P, method=\"hybr\", optimizer_kwargs=dict(tol=1e-8))\n",
+    "\n",
+    "    return P_star, success"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fff5780",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Here is what the user might actually face in reality. She called `find_optimal_P`, passing the price variable, her model of expected sales, and the marginal cost. We then do the best we can, returning either the exact linear solution, or the symbolic root finder, then compile a funtion she can use to do analysis.\n",
+    "\n",
+    "In a real package, we'd probably want to hide the compilation too!\n",
+    "\n",
+    "Note that we have to use the `on_unused_input` argument to `pytensor.function`. By default, if you provide a root variable that Pytensor does not require to compute the outputs, it will raise an error. Here, we need the prices as initial values of the root finder, but we don't require them if we find a linear solution. Since we don't know what will happen, we always require the prices to be passed in. In the linear case, they are just ignored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c294e153",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "optimal_P, success = find_optimal_P_v2(price, expected_sales, mc)\n",
+    "P_star_fn = pytensor.function(\n",
+    "    [time, price, *params, mc], [optimal_P, success], on_unused_input=\"ignore\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d685c25",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "P_star, success_flag = P_star_fn(\n",
+    "    time_value, np.zeros_like(time_value), **optim_params, marginal_cost=5.0\n",
+    ")\n",
+    "Q_star = sales_fn(time_value, P_star, **optim_params)\n",
+    "\n",
+    "plt.plot(\n",
+    "    profit_fn(P_star, Q_star, marginal_cost=5.0),\n",
+    "    lw=2,\n",
+    "    label=\"Profits under optimal price\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    profit_fn(\n",
+    "        prices_obs, sales_fn(time_value, prices_obs, **optim_params), marginal_cost=5.0\n",
+    "    ),\n",
+    "    lw=2,\n",
+    "    label=\"Observed profits\",\n",
+    ")\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d3eb601",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "# Handling Complex Scenarios\n",
+    "\n",
+    "Again, the power of pytensor is that it can keep handling more and more complex situations without much additional effort.\n",
+    "\n",
+    "A common situation in these types of price optimization problems is that decision makers have already committed to a certain price at certain times. So you're not actually able to freely optimize in every period. Instead, you have to do the optimization of certain periods, subject to fixed prices in other periods.\n",
+    "\n",
+    "In the final example, we suppose that we are free to optimize prices for 150 periods, between $t=100$ and $t=250$. Otherwise, we have to take a fixed price.\n",
+    "\n",
+    "To handle this, we extend the `find_optimal_P` function one more time, to check if the incoming `P` is itself a function of some underlying input. For simplicity, the only function we allow is `SetSubtensor`, which does what it adverties. If we find that `P` is the result of a `SetSubtensor` Op, we reach into that Op and grab the input, then do all the optimization with respect to *that* input, rather than P itself.\n",
+    "\n",
+    "This is a case where it would be very difficult to handle without access to the underlying computational graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f29c0ce-6b78-401a-b62c-26893d5ab57e",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def find_optimal_P_v3(P, Q, mc):\n",
+    "    pi = (Q * (P - mc)).sum()\n",
+    "    # Check which root inputs of P are actually free variables\n",
+    "    [choice_variable] = explicit_graph_inputs([P])\n",
+    "\n",
+    "    # Whatever we found, optimize it\n",
+    "    dpi_dP = pt.grad(pi, choice_variable)\n",
+    "    expr = rewrite_graph(dpi_dP, include=(\"canonicalize\",))\n",
+    "    P_star, success = find_exact_linear_solution(expr)\n",
+    "\n",
+    "    if P_star is None:\n",
+    "        print(\"No exact solution avaiable, using numerical solver\")\n",
+    "        P_star, success = root(\n",
+    "            expr, choice_variable, method=\"lm\", optimizer_kwargs=dict(tol=1e-8)\n",
+    "        )\n",
+    "\n",
+    "    # If we optimized with respect to an input to P, sustitute the optimal value back into\n",
+    "    # the original P\n",
+    "    P_star = graph_replace(P, {choice_variable: P_star})\n",
+    "\n",
+    "    return P_star, success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec8ec6cf-effe-46cc-990c-c5b33562017d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "free_prices = pt.vector(\"free_prices\")\n",
+    "price_partially_fixed = pt.as_tensor(prices_obs)[100:250].set(free_prices)\n",
+    "\n",
+    "sales_partially_fixed = graph_replace(expected_sales, {price: price_partially_fixed})\n",
+    "\n",
+    "optimal_P_constrained, success = find_optimal_P_v3(\n",
+    "    price_partially_fixed, sales_partially_fixed, mc\n",
+    ")\n",
+    "\n",
+    "P_star_constrained_fn = pytensor.function(\n",
+    "    [time, price, free_prices, *params, mc],\n",
+    "    [optimal_P_constrained, success],\n",
+    "    on_unused_input=\"ignore\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b2ebd77-0a60-4959-88a7-893ac43c4ef5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "P_star_constrained, _ = P_star_constrained_fn(\n",
+    "    time_value, prices_obs, np.zeros(150), **optim_params, marginal_cost=5.0\n",
+    ")\n",
+    "Q_star = sales_fn(time_value, P_star_constrained, **optim_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de1e6f8e",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because we've inserted this `SetSubtensor` Op between the price and the model, our logic for finding $a + bP$ breaks. That fine though, we just drop back to the numerical solver."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd7aff7a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As promised, we now only optimize the period between $t=100$ and $t=250$. Otherwise, we're constrained to accept the observed price."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58db2a42",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(\n",
+    "    profit_fn(P_star_constrained, Q_star, marginal_cost=5.0),\n",
+    "    lw=3,\n",
+    "    label=\"Profit under optimized price\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    profit_fn(\n",
+    "        prices_obs, sales_fn(time_value, prices_obs, **optim_params), marginal_cost=5.0\n",
+    "    ),\n",
+    "    lw=3,\n",
+    "    ls=\"--\",\n",
+    "    label=\"Observed profit\",\n",
+    ")\n",
+    "plt.legend(fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d65e6c91",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Summary and Conclusion\n",
+    "\n",
+    "- Theano was a unique package, a symbolic math library that got pidgin holed as a deep learning library.\n",
+    "- The ability to symbolically manipulate programs is extremely powerful, enabling optimization, user-focused APIs, and transpilation.\n",
+    "- Pytensor continues the legacy of Theano, continuing to add more rewrites and optimizations, improve documentation, extend to new compiled backends, and make entirely new features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04947ea8",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "One exciting example is an xarray inspired named dimensions API for more human-readable tensor manipulations:\n",
+    "\n",
+    "```py\n",
+    "\n",
+    "import pytensor.xtensor as ptx\n",
+    "\n",
+    "logits = ptx.xtensor(dims=[\"user\", \"choice\"])\n",
+    "probs = ptx.softmax(logits, dim=\"choice\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5815385",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Existing Pytensor Ecosystem\n",
+    "\n",
+    "\n",
+    "- [PyMC](https://github.com/pymc-devs/pymc)\n",
+    "- [pymc-marketing](https://github.com/pymc-labs/pymc-marketing)\n",
+    "- [CausalPy](https://github.com/pymc-labs/causalpy)\n",
+    "- [gEconpy](https://github.com/jessegrabowski/gEconpy)\n",
+    "- [pyhs3](https://github.com/scipp-atlas/pyhs3)\n",
+    "- [HSSM](https://github.com/lnccbrown/HSSM)\n",
+    "- [Celmech](https://github.com/shadden/celmech)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28e55d35",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}