fix code references

Author: juanitorduz

examples/time_series/Time_Series_Generative_Graph.ipynb

@@ -21,7 +21,7 @@
    "id": "91d1fcf3",
    "metadata": {},
    "source": [
-    "In This notebook, we show to model and fit a time series model starting from a generative graph. In particular, we explain how to use {func}`~pytensor.scan` to loop efficiently inside a PyMC model.\n",
+    "In This notebook, we show to model and fit a time series model starting from a generative graph. In particular, we explain how to use {func}`~pytensor.scan.basic.scan` to loop efficiently inside a PyMC model.\n",
     "\n",
     "For this example, we consider an autoregressive model AR(2). Recall that an AR(2) model is defined as:\n",
     "\n",
@@ -79,9 +79,9 @@
    "source": [
     "## Define AR(2) Process\n",
     "\n",
-    "We start by encoding the generative graph of the AR(2) model as a function `ar_dist`. The strategy is to pass this function as a custom distribution via {class}`~pm.CustomDist` inside a PyMC model. \n",
+    "We start by encoding the generative graph of the AR(2) model as a function `ar_dist`. The strategy is to pass this function as a custom distribution via {class}`~pymc.CustomDist` inside a PyMC model. \n",
     "\n",
-    "We need to specify the initial state (`ar_init`), the autoregressive coefficients (`rho`), and the standard deviation of the noise (`sigma`). Given such parameters, we can define the generative graph of the AR(2) model using the  {func}`~pytensor.scan` operation."
+    "We need to specify the initial state (`ar_init`), the autoregressive coefficients (`rho`), and the standard deviation of the noise (`sigma`). Given such parameters, we can define the generative graph of the AR(2) model using the  {func}`~pytensor.scan.basic.scan` operation."
    ]
   },
   {
@@ -173,35 +173,35 @@
        "<text text-anchor=\"middle\" x=\"310\" y=\"-153.7\" font-family=\"Times,serif\" font-size=\"14.00\">CustomDist_ar_dist</text>\n",
        "</g>\n",
        "<!-- rho&#45;&gt;ar_dist -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
        "<title>rho&#45;&gt;ar_dist</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M189.45,-267.11C208.64,-252.64 233.46,-233.94 255.57,-217.27\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"257.45,-220.24 263.33,-211.42 253.24,-214.65 257.45,-220.24\"/>\n",
        "</g>\n",
-       "<!-- ar_init -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>ar_init</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"57\" cy=\"-292.57\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"57\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">ar_init</text>\n",
-       "<text text-anchor=\"middle\" x=\"57\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"57\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
        "<!-- ar_init_obs -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
+       "<g id=\"node2\" class=\"node\">\n",
        "<title>ar_init_obs</title>\n",
        "<path fill=\"lightgrey\" stroke=\"black\" d=\"M91.62,-204C91.62,-204 28.38,-204 28.38,-204 22.38,-204 16.38,-198 16.38,-192 16.38,-192 16.38,-158.5 16.38,-158.5 16.38,-152.5 22.38,-146.5 28.38,-146.5 28.38,-146.5 91.62,-146.5 91.62,-146.5 97.62,-146.5 103.62,-152.5 103.62,-158.5 103.62,-158.5 103.62,-192 103.62,-192 103.62,-198 97.62,-204 91.62,-204\"/>\n",
        "<text text-anchor=\"middle\" x=\"60\" y=\"-186.7\" font-family=\"Times,serif\" font-size=\"14.00\">ar_init_obs</text>\n",
        "<text text-anchor=\"middle\" x=\"60\" y=\"-170.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text text-anchor=\"middle\" x=\"60\" y=\"-153.7\" font-family=\"Times,serif\" font-size=\"14.00\">MutableData</text>\n",
        "</g>\n",
+       "<!-- ar_init -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>ar_init</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"57\" cy=\"-292.57\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"57\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">ar_init</text>\n",
+       "<text text-anchor=\"middle\" x=\"57\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"57\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
        "<!-- ar_init&#45;&gt;ar_init_obs -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>ar_init&#45;&gt;ar_init_obs</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M58.04,-251.56C58.35,-239.85 58.68,-227.07 58.98,-215.41\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"62.47,-215.81 59.23,-205.72 55.47,-215.63 62.47,-215.81\"/>\n",
        "</g>\n",
        "<!-- ar_init&#45;&gt;ar_dist -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
        "<title>ar_init&#45;&gt;ar_dist</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M87.14,-264.52C93.4,-259.83 100.18,-255.36 107,-251.91 149.34,-230.51 165.52,-240.4 210,-223.91 220.35,-220.07 231.08,-215.56 241.5,-210.87\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"242.8,-214.13 250.42,-206.77 239.88,-207.77 242.8,-214.13\"/>\n",
@@ -229,7 +229,7 @@
        "<text text-anchor=\"middle\" x=\"310\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
        "</g>\n",
        "<!-- sigma&#45;&gt;ar_dist -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
        "<title>sigma&#45;&gt;ar_dist</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M310,-251.56C310,-243.78 310,-235.52 310,-227.44\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"313.5,-227.7 310,-217.7 306.5,-227.7 313.5,-227.7\"/>\n",
@@ -258,7 +258,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x127a93450>"
+       "<graphviz.graphs.Digraph at 0x1353ab310>"
       ]
      },
      "execution_count": 3,
@@ -535,7 +535,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:15&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 00:17&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -550,7 +550,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 26 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 28 seconds.\n"
      ]
     }
    ],
@@ -716,7 +716,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [4000/4000 00:29&lt;00:00]\n",
+       "      100.00% [4000/4000 00:30&lt;00:00]\n",
        "    </div>\n",
        "    "
       ],
@@ -881,11 +881,11 @@
       "\n",
       "pytensor: 2.18.6\n",
       "\n",
+      "arviz     : 0.17.0\n",
+      "matplotlib: 3.8.2\n",
+      "pymc      : 5.10.3\n",
       "numpy     : 1.26.3\n",
       "pytensor  : 2.18.6\n",
-      "pymc      : 5.10.3\n",
-      "matplotlib: 3.8.2\n",
-      "arviz     : 0.17.0\n",
       "\n",
       "Watermark: 2.4.3\n",
       "\n"

examples/time_series/Time_Series_Generative_Graph.myst.md

@@ -21,7 +21,7 @@ kernelspec:
 
 +++
 
-In This notebook, we show to model and fit a time series model starting from a generative graph. In particular, we explain how to use {func}`~pytensor.scan` to loop efficiently inside a PyMC model.
+In This notebook, we show to model and fit a time series model starting from a generative graph. In particular, we explain how to use {func}`~pytensor.scan.basic.scan` to loop efficiently inside a PyMC model.
 
 For this example, we consider an autoregressive model AR(2). Recall that an AR(2) model is defined as:
 
@@ -57,9 +57,9 @@ rng = np.random.default_rng(42)
 
 ## Define AR(2) Process
 
-We start by encoding the generative graph of the AR(2) model as a function `ar_dist`. The strategy is to pass this function as a custom distribution via {class}`~pm.CustomDist` inside a PyMC model. 
+We start by encoding the generative graph of the AR(2) model as a function `ar_dist`. The strategy is to pass this function as a custom distribution via {class}`~pymc.CustomDist` inside a PyMC model. 
 
-We need to specify the initial state (`ar_init`), the autoregressive coefficients (`rho`), and the standard deviation of the noise (`sigma`). Given such parameters, we can define the generative graph of the AR(2) model using the  {func}`~pytensor.scan` operation.
+We need to specify the initial state (`ar_init`), the autoregressive coefficients (`rho`), and the standard deviation of the noise (`sigma`). Given such parameters, we can define the generative graph of the AR(2) model using the  {func}`~pytensor.scan.basic.scan` operation.
 
 ```{code-cell} ipython3
 lags = 2  # Number of lags