diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index cabf5e2c1..a90386a2f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,7 +9,7 @@ repos:
     hooks:
     - id: isort
 -   repo: https://github.com/ambv/black
-    rev: 23.9.1
+    rev: 23.10.1
     hooks:
     - id: black
       language_version: python3
@@ -22,10 +22,10 @@ repos:
     hooks:
     -   id: pydocstyle
         args: ['--ignore', 'D213,D100,D203,D104']
-        files: ^pyhgf/
+        files: ^src/
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: 'v1.6.0'
+    rev: 'v1.6.1'
     hooks:
     - id: mypy 
-      files: ^pyhgf/
+      files: ^src/
       args: [--ignore-missing-imports]
\ No newline at end of file
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 68ff3bde0..114c2214d 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -62,31 +62,51 @@ Propagate prediction errors to the value and volatility parents of a given node.
 Binary nodes
 ~~~~~~~~~~~~
 
-.. currentmodule:: pyhgf.updates.prediction_error.binary
+.. currentmodule:: pyhgf.updates.prediction_error.inputs.binary
 
 .. autosummary::
-   :toctree: generated/pyhgf.updates.prediction_error.binary
+   :toctree: generated/pyhgf.updates.prediction_error.inputs.binary
 
-    prediction_error_mean_value_parent
-    prediction_error_precision_value_parent
-    prediction_error_value_parent
     prediction_error_input_value_parent
     input_surprise_inf
     input_surprise_reg
 
+.. currentmodule:: pyhgf.updates.prediction_error.nodes.binary
+
+.. autosummary::
+   :toctree: generated/pyhgf.updates.prediction_error.nodes.binary
+
+    prediction_error_mean_value_parent
+    prediction_error_precision_value_parent
+    prediction_error_value_parent
+
 Continuous nodes
 ~~~~~~~~~~~~~~~~
 
-.. currentmodule:: pyhgf.updates.prediction_error.continuous
+Updating continuous input nodes.
+
+.. currentmodule:: pyhgf.updates.prediction_error.inputs.continuous
 
 .. autosummary::
-   :toctree: generated/pyhgf.updates.prediction_error.continuous
+   :toctree: generated/pyhgf.updates.prediction_error.inputs.continuous
+
+    prediction_error_input_precision_value_parent
+    prediction_error_input_precision_volatility_parent
+    prediction_error_input_mean_volatility_parent
+    prediction_error_input_mean_value_parent
+
+
+Updating continuous state nodes.
+
+.. currentmodule:: pyhgf.updates.prediction_error.nodes.continuous
+
+.. autosummary::
+   :toctree: generated/pyhgf.updates.prediction_error.nodes.continuous
 
     prediction_error_mean_value_parent
     prediction_error_precision_value_parent
     prediction_error_precision_volatility_parent
     prediction_error_mean_volatility_parent
-    prediction_error_input_mean_value_parent
 
 Prediction steps
 ================
diff --git a/docs/source/notebooks/1.2-Categorical_HGF.ipynb b/docs/source/notebooks/1.2-Categorical_HGF.ipynb
index 795f521e7..fd5e8b04f 100644
--- a/docs/source/notebooks/1.2-Categorical_HGF.ipynb
+++ b/docs/source/notebooks/1.2-Categorical_HGF.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 32,
    "id": "d2c1e257-91ab-455d-a3ea-0ac0136797ed",
    "metadata": {
     "tags": [
@@ -31,13 +31,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 33,
    "id": "ee358d37-b215-4547-8c5b-a41b09d030ee",
    "metadata": {},
    "outputs": [],
    "source": [
     "from pyhgf.model import HGF\n",
     "import numpy as np\n",
+    "import pymc as pm\n",
+    "import arviz as az\n",
     "from pyhgf import load_data\n",
     "import jax.numpy as jnp\n",
     "from jax import jit, grad, vjp\n",
@@ -71,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 34,
    "id": "dba7fabb-e5d9-48d6-8c8e-2fecc7df28be",
    "metadata": {},
    "outputs": [],
@@ -95,13 +97,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 35,
    "id": "f37e6acb-ae8e-4aea-8914-5333cbe46921",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x300 with 1 Axes>"
       ]
@@ -135,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 36,
    "id": "6511fd24-48a0-4913-9cb2-2ed3dba57f4f",
    "metadata": {},
    "outputs": [],
@@ -164,18 +166,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 37,
    "id": "1ae2e67a-0b7b-462b-89b7-0c1ec1fb4262",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "categorical_hgf = (\n",
     "    HGF(model_type=None, verbose=False)\n",
@@ -183,14 +177,13 @@
     "        kind=\"categorical\", \n",
     "        categorical_parameters={\"n_categories\": 10}, \n",
     "        binary_parameters={\"omega_2\": -2.0}\n",
-    "    )\n",
-    "    .init()\n",
+    "    ).init()\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 38,
    "id": "2806c2d9-e6a9-4113-829f-1f0616a31b4b",
    "metadata": {},
    "outputs": [
@@ -644,10 +637,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.sources.Source at 0x7fc85ca51a30>"
+       "<graphviz.sources.Source at 0x7f00d0172670>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 39,
    "id": "6e27b3d9-1b10-4cf9-9074-725a9638345d",
    "metadata": {
     "editable": true,
@@ -682,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 40,
    "id": "3e76f37e-36b0-49d4-8169-86f08b309e0d",
    "metadata": {
     "editable": true,
@@ -694,7 +687,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x900 with 6 Axes>"
       ]
@@ -706,7 +699,7 @@
    "source": [
     "fig, axs = plt.subplots(nrows=5, figsize=(12, 9), sharex=True)\n",
     "plot_nodes(categorical_hgf, node_idxs=31, axs=axs[0])\n",
-    "axs[1].imshow(categorical_hgf.node_trajectories[0][\"mu\"].T, interpolation=\"none\", aspect=\"auto\")\n",
+    "axs[1].imshow(categorical_hgf.node_trajectories[0][\"mean\"].T, interpolation=\"none\", aspect=\"auto\")\n",
     "axs[1].set_title(\"Mean of the implied Dirichlet distribution\", loc=\"left\")\n",
     "axs[1].set_ylabel(\"Categories\")\n",
     "\n",
@@ -778,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 52,
    "id": "895b59fc-9e49-4848-bc07-59325e34b5d4",
    "metadata": {},
    "outputs": [],
@@ -786,15 +779,13 @@
     "def categorical_surprise(omega_2, hgf, input_data):\n",
     "\n",
     "    # replace with a new omega in the model\n",
-    "    for va_pa in hgf.edges[0].value_parents:\n",
-    "        for va_pa_va_pa in hgf.edges[va_pa].value_parents:\n",
-    "            for va_pa_va_pa_va_pa in hgf.edges[va_pa_va_pa].value_parents:\n",
-    "                hgf.attributes[va_pa_va_pa_va_pa][\"omega\"] = omega_2\n",
+    "    for idx in np.arange(21, 31):\n",
+    "        hgf.attributes[idx][\"tonic_volatility\"] = omega_2\n",
     "\n",
-    "    # fit the model to new data\n",
+    "    # run the model forward again\n",
     "    hgf.input_data(input_data=input_data.T)\n",
     "\n",
-    "    # compute the surprises from KL divergences\n",
+    "    # compute the surprises using KL divergences\n",
     "    surprise = hgf.node_trajectories[0][\"kl_divergence\"][2:].sum()\n",
     "\n",
     "    # return an infinite surprise if the model could not fit at any point\n",
@@ -817,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 53,
    "id": "05bcec35-1b57-4593-9285-b773f0f9e5c9",
    "metadata": {},
    "outputs": [],
@@ -833,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 54,
    "id": "191f65ab-6b82-4de6-9a3a-940f5d6c7400",
    "metadata": {
     "editable": true,
@@ -897,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 55,
    "id": "ccf51208-980b-4ccd-9b2a-cae1525da3f0",
    "metadata": {
     "editable": true,
@@ -906,12 +897,91 @@
     },
     "tags": []
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (2 chains in 4 jobs)\n",
+      "NUTS: [omega_2]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='1121' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      28.02% [1121/4000 31:58&lt;1:22:06 Sampling 2 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Not enough samples to build a trace.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[55], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m omega_2 \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mNormal(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124momega_2\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2.0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m      3\u001b[0m pm\u001b[38;5;241m.\u001b[39mPotential(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhgf\u001b[39m\u001b[38;5;124m\"\u001b[39m, custom_op(omega_2))\n\u001b[0;32m----> 4\u001b[0m categorical_idata \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/mambaforge/envs/pyhgf_dev/lib/python3.9/site-packages/pymc/sampling/mcmc.py:791\u001b[0m, in \u001b[0;36msample\u001b[0;34m(draws, tune, chains, cores, random_seed, progressbar, step, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, model, **kwargs)\u001b[0m\n\u001b[1;32m    787\u001b[0m t_sampling \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m t_start\n\u001b[1;32m    789\u001b[0m \u001b[38;5;66;03m# Packaging, validating and returning the result was extracted\u001b[39;00m\n\u001b[1;32m    790\u001b[0m \u001b[38;5;66;03m# into a function to make it easier to test and refactor.\u001b[39;00m\n\u001b[0;32m--> 791\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_sample_return\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtraces\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtraces\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtune\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtune\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mt_sampling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt_sampling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdiscard_tuned_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiscard_tuned_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute_convergence_checks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute_convergence_checks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_inferencedata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_inferencedata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeep_warning_stat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_warning_stat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43midata_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midata_kwargs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/mambaforge/envs/pyhgf_dev/lib/python3.9/site-packages/pymc/sampling/mcmc.py:822\u001b[0m, in \u001b[0;36m_sample_return\u001b[0;34m(run, traces, tune, t_sampling, discard_tuned_samples, compute_convergence_checks, return_inferencedata, keep_warning_stat, idata_kwargs, model)\u001b[0m\n\u001b[1;32m    820\u001b[0m \u001b[38;5;66;03m# Pick and slice chains to keep the maximum number of samples\u001b[39;00m\n\u001b[1;32m    821\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m discard_tuned_samples:\n\u001b[0;32m--> 822\u001b[0m     traces, length \u001b[38;5;241m=\u001b[39m \u001b[43m_choose_chains\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraces\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtune\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    823\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    824\u001b[0m     traces, length \u001b[38;5;241m=\u001b[39m _choose_chains(traces, \u001b[38;5;241m0\u001b[39m)\n",
+      "File \u001b[0;32m~/mambaforge/envs/pyhgf_dev/lib/python3.9/site-packages/pymc/backends/base.py:601\u001b[0m, in \u001b[0;36m_choose_chains\u001b[0;34m(traces, tune)\u001b[0m\n\u001b[1;32m    599\u001b[0m lengths \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(trace) \u001b[38;5;241m-\u001b[39m tune) \u001b[38;5;28;01mfor\u001b[39;00m trace \u001b[38;5;129;01min\u001b[39;00m traces]\n\u001b[1;32m    600\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28msum\u001b[39m(lengths):\n\u001b[0;32m--> 601\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot enough samples to build a trace.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    603\u001b[0m idxs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(lengths)\n\u001b[1;32m    604\u001b[0m l_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(lengths)[idxs]\n",
+      "\u001b[0;31mValueError\u001b[0m: Not enough samples to build a trace."
+     ]
+    }
+   ],
+   "source": [
+    "with pm.Model() as model:\n",
+    "    omega_2 = pm.Normal(\"omega_2\", -2.0, 2)\n",
+    "    pm.Potential(\"hgf\", custom_op(omega_2))\n",
+    "    categorical_idata = pm.sample(chains=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bce093e9-1ab0-4ea1-99e6-a83b68331b9c",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# with pm.Model() as model:\n",
-    "#     omega_2 = pm.Normal(\"omega_2\", -2.0, 2)\n",
-    "#     pm.Potential(\"hgf\", custom_op(omega_2))\n",
-    "#     categorical_idata = pm.sample(chains=2)"
+    "az.plot_trace(categorical_idata)"
    ]
   },
   {
@@ -942,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "25f8522f-9d42-47fd-a6bc-87ffc0dfd866",
    "metadata": {
     "editable": true,
@@ -952,34 +1022,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Last updated: Wed Aug 30 2023\n",
-      "\n",
-      "Python implementation: CPython\n",
-      "Python version       : 3.9.16\n",
-      "IPython version      : 8.14.0\n",
-      "\n",
-      "pyhgf : 0.0.6\n",
-      "jax   : 0.4.14\n",
-      "jaxlib: 0.4.14\n",
-      "\n",
-      "pytensor  : 2.12.3\n",
-      "seaborn   : 0.12.2\n",
-      "matplotlib: 3.7.1\n",
-      "jax       : 0.4.14\n",
-      "sys       : 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:39:03) \n",
-      "[GCC 11.3.0]\n",
-      "numpy     : 1.22.0\n",
-      "\n",
-      "Watermark: 2.4.3\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib"
diff --git a/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb
new file mode 100644
index 000000000..ef2136bd0
--- /dev/null
+++ b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb
@@ -0,0 +1,536 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "442ef7e2-9591-40be-aae1-0160131b4bfb",
+   "metadata": {},
+   "source": [
+    "(example_1)=\n",
+    "# Example 2: Estimating the mean and precision of an input node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e329db3-38e9-4664-9043-cf042ec1a562",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    ! pip install pyhgf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "826e4179-795a-4cf0-a2bd-8b1882f68c18",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from pyhgf.distribution import HGFDistribution\n",
+    "from pyhgf.model import HGF\n",
+    "import numpy as np\n",
+    "import pymc as pm\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56e7df28-7d15-4add-99fe-c322cde28353",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Where the standard continuous HGF assumes a known precision in the input node (usually set to something high), this assumption can be relaxed and the filter can also try to estimate this quantity from the data. In this notebook, we demonstrate how we can infer the value of the mean, of the precision, or both value at the same time, using the appropriate value and volatility coupling parents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34dff83a-1035-43c6-b89b-85ddd2acdebe",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Unkown mean, known precision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d82a1e1-8956-4884-a5e7-fa003629b596",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "```{hint}\n",
+    "The {ref}`continuous_hgf` is an example of a model assuming a continuous input with known precision and unknown mean. It is further assumed that the mean is changing overtime, and we want the model to track this rate of change by adding a volatility node on the top of the value parent (two-level continuous HGF), and event track the rate of change of this rate of change by adding another volatility parent (three-level continuous HGF).\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ea041ab8-e945-47a6-9791-91267b39a1ba",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dist_mean, dist_std = 5, 1\n",
+    "input_data = np.random.normal(loc=dist_mean, scale=dist_std, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0ec605f8-b95e-48a8-856d-d8e1b04f4852",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing a network with custom node structure.\n",
+      "... Create the update sequence from the network structure.\n",
+      "... Create the belief propagation function.\n",
+      "... Cache the belief propagation function.\n",
+      "Adding 1000 new observations.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 8.0.5 (20230506.1012)\n",
+       " -->\n",
+       "<!-- Title: hgf&#45;nodes Pages: 1 -->\n",
+       "<svg width=\"76pt\" height=\"116pt\"\n",
+       " viewBox=\"0.00 0.00 75.69 116.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 112)\">\n",
+       "<title>hgf&#45;nodes</title>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-112 71.69,-112 71.69,4 -4,4\"/>\n",
+       "<!-- x_0 -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>x_0</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"33.85\" cy=\"-18\" rx=\"33.85\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"33.85\" y=\"-13.32\" font-family=\"Times,serif\" font-size=\"14.00\">Co&#45;0</text>\n",
+       "</g>\n",
+       "<!-- x_1 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>x_1</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"33.85\" cy=\"-90\" rx=\"18\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"33.85\" y=\"-85.33\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
+       "</g>\n",
+       "<!-- x_1&#45;&gt;x_0 -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>x_1&#45;&gt;x_0</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M33.85,-71.7C33.85,-64.24 33.85,-55.32 33.85,-46.97\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"37.35,-47.1 33.85,-37.1 30.35,-47.1 37.35,-47.1\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.sources.Source at 0x7f7b04891730>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_hgf = (\n",
+    "    HGF(model_type=None)\n",
+    "    .add_input_node(kind=\"continuous\", continuous_parameters={'continuous_precision': 1})\n",
+    "    .add_value_parent(children_idxs=[0], tonic_volatility=-8.0)\n",
+    "    .init()\n",
+    ").input_data(input_data)\n",
+    "mean_hgf.plot_network()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "567d21ef-e37a-4f74-bea5-e52db864429b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "```{note}\n",
+    "We are setting the tonic volatility to something low for visualization purposes, but changing this value can make the model learn in fewer iterations.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3cd8a2e6-6df5-48f2-9873-1715d3823eb0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the nodes trajectories\n",
+    "df = mean_hgf.to_pandas()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "\n",
+    "x = np.linspace(-10, 10, 1000)\n",
+    "for i, color in zip([0, 2, 5, 10, 50, 500], plt.cm.Greys(np.linspace(.2, 1, 6))):\n",
+    "\n",
+    "    # extract the sufficient statistics from the input node (and parents)\n",
+    "    mean = df.x_1_expected_mean.iloc[i]\n",
+    "    std = np.sqrt(\n",
+    "        1/(mean_hgf.attributes[0][\"expected_precision\"])\n",
+    "    )\n",
+    "\n",
+    "    # the model expectations\n",
+    "    ax.plot(x, norm(mean, std).pdf(x), color=color, label=i)\n",
+    "\n",
+    "\n",
+    "# the sampling distribution\n",
+    "ax.fill_between(x, norm(dist_mean, dist_std).pdf(x), color=\"#582766\", alpha=.2)\n",
+    "\n",
+    "ax.legend(title=\"Iterations\")\n",
+    "ax.set_xlabel(\"Input (u)\")\n",
+    "ax.set_ylabel(\"Density\")\n",
+    "plt.grid(linestyle=\":\")\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3efcf2a3-72fb-4706-b412-1366a781b234",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Kown mean, unknown precision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97141a28-777e-4adc-9ca7-60d565579e54",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Unkown mean, unknown precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7442662d-a760-4f89-baed-9bcd4cc5162a",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dist_mean, dist_std = 5, 1\n",
+    "input_data = np.random.normal(loc=dist_mean, scale=dist_std, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ae086ae0-fa93-4511-9a07-e190f72ee680",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing a network with custom node structure.\n",
+      "... Create the update sequence from the network structure.\n",
+      "... Create the belief propagation function.\n",
+      "... Cache the belief propagation function.\n",
+      "Adding 1000 new observations.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 8.0.5 (20230506.1012)\n",
+       " -->\n",
+       "<!-- Title: hgf&#45;nodes Pages: 1 -->\n",
+       "<svg width=\"90pt\" height=\"188pt\"\n",
+       " viewBox=\"0.00 0.00 90.00 188.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n",
+       "<title>hgf&#45;nodes</title>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-184 86,-184 86,4 -4,4\"/>\n",
+       "<!-- x_0 -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>x_0</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"41\" cy=\"-18\" rx=\"33.85\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"41\" y=\"-13.32\" font-family=\"Times,serif\" font-size=\"14.00\">Co&#45;0</text>\n",
+       "</g>\n",
+       "<!-- x_1 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>x_1</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"18\" cy=\"-90\" rx=\"18\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"18\" y=\"-85.33\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
+       "</g>\n",
+       "<!-- x_1&#45;&gt;x_0 -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>x_1&#45;&gt;x_0</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M23.45,-72.41C26.02,-64.59 29.15,-55.06 32.05,-46.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"35.63,-47.55 35.43,-36.96 28.98,-45.36 35.63,-47.55\"/>\n",
+       "</g>\n",
+       "<!-- x_2 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>x_2</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"64\" cy=\"-162\" rx=\"18\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"64\" y=\"-157.32\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
+       "</g>\n",
+       "<!-- x_2&#45;&gt;x_0 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>x_2&#45;&gt;x_0</title>\n",
+       "<path fill=\"none\" stroke=\"gray\" stroke-dasharray=\"5,2\" d=\"M61.23,-143.87C57.24,-119.24 49.85,-73.64 45.19,-44.88\"/>\n",
+       "<ellipse fill=\"gray\" stroke=\"gray\" cx=\"44.5\" cy=\"-40.63\" rx=\"4\" ry=\"4\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.sources.Source at 0x7f7afc45e580>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_precision_hgf = (\n",
+    "    HGF(model_type=None)\n",
+    "    .add_input_node(kind=\"continuous\", continuous_parameters={'continuous_precision': 0.01})\n",
+    "    .add_value_parent(children_idxs=[0], tonic_volatility=-6.0)\n",
+    "    .add_volatility_parent(children_idxs=[0], tonic_volatility=-6.0)\n",
+    "    .init()\n",
+    ").input_data(input_data)\n",
+    "mean_precision_hgf.plot_network()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b502f49e-f78d-44f4-8f7a-a715e3bf611f",
+   "metadata": {
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the nodes trajectories\n",
+    "df = mean_precision_hgf.to_pandas()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "\n",
+    "x = np.linspace(-10, 10, 1000)\n",
+    "for i, color in zip(range(0, 150, 15), plt.cm.Greys(np.linspace(.2, 1, 10))):\n",
+    "\n",
+    "    # extract the sufficient statistics from the input node (and parents)\n",
+    "    mean = df.x_1_expected_mean.iloc[i]\n",
+    "    std = np.sqrt(\n",
+    "        1/(mean_precision_hgf.attributes[0][\"expected_precision\"] * (1/np.exp(df.x_2_expected_mean.iloc[i])))\n",
+    ")\n",
+    "\n",
+    "    # the model expectations\n",
+    "    ax.plot(x, norm(mean, std).pdf(x), color=color, label=i)\n",
+    "\n",
+    "\n",
+    "# the sampling distribution\n",
+    "ax.fill_between(x, norm(dist_mean, dist_std).pdf(x), color=\"#582766\", alpha=.2)\n",
+    "\n",
+    "ax.legend(title=\"Iterations\")\n",
+    "ax.set_xlabel(\"Input (u)\")\n",
+    "ax.set_ylabel(\"Density\")\n",
+    "plt.grid(linestyle=\":\")\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6da1aaa4-5bbe-4579-ad79-1601d79dd09b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## System configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3738137f-7eaf-49b3-a87a-c542ad949b7c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Thu Nov 02 2023\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.9.16\n",
+      "IPython version      : 8.14.0\n",
+      "\n",
+      "pyhgf : 0.0.9\n",
+      "jax   : 0.4.14\n",
+      "jaxlib: 0.4.14\n",
+      "\n",
+      "seaborn   : 0.12.2\n",
+      "arviz     : 0.16.1\n",
+      "matplotlib: 3.7.1\n",
+      "numpy     : 1.22.0\n",
+      "sys       : 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:39:03) \n",
+      "[GCC 11.3.0]\n",
+      "pymc      : 5.5.0\n",
+      "\n",
+      "Watermark: 2.4.3\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md:myst"
+  },
+  "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md
new file mode 100644
index 000000000..f796c31fb
--- /dev/null
+++ b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md
@@ -0,0 +1,202 @@
+---
+jupytext:
+  formats: ipynb,md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.15.1
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+(example_1)=
+# Example 2: Estimating the mean and precision of an input node
+
+```{code-cell} ipython3
+%%capture
+import sys
+if 'google.colab' in sys.modules:
+    ! pip install pyhgf
+```
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
+from pyhgf.distribution import HGFDistribution
+from pyhgf.model import HGF
+import numpy as np
+import pymc as pm
+import arviz as az
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy.stats import norm
+```
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+Where the standard continuous HGF assumes a known precision in the input node (usually set to something high), this assumption can be relaxed and the filter can also try to estimate this quantity from the data. In this notebook, we demonstrate how we can infer the value of the mean, of the precision, or both value at the same time, using the appropriate value and volatility coupling parents.
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+## Unkown mean, known precision
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+```{hint}
+The {ref}`continuous_hgf` is an example of a model assuming a continuous input with known precision and unknown mean. It is further assumed that the mean is changing overtime, and we want the model to track this rate of change by adding a volatility node on the top of the value parent (two-level continuous HGF), and event track the rate of change of this rate of change by adding another volatility parent (three-level continuous HGF).
+```
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
+dist_mean, dist_std = 5, 1
+input_data = np.random.normal(loc=dist_mean, scale=dist_std, size=1000)
+```
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
+mean_hgf = (
+    HGF(model_type=None)
+    .add_input_node(kind="continuous", continuous_parameters={'continuous_precision': 1})
+    .add_value_parent(children_idxs=[0], tonic_volatility=-8.0)
+    .init()
+).input_data(input_data)
+mean_hgf.plot_network()
+```
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+```{note}
+We are setting the tonic volatility to something low for visualization purposes, but changing this value can make the model learn in fewer iterations.
+```
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+tags: [hide-input]
+---
+# get the nodes trajectories
+df = mean_hgf.to_pandas()
+
+fig, ax = plt.subplots(figsize=(12, 5))
+
+x = np.linspace(-10, 10, 1000)
+for i, color in zip([0, 2, 5, 10, 50, 500], plt.cm.Greys(np.linspace(.2, 1, 6))):
+
+    # extract the sufficient statistics from the input node (and parents)
+    mean = df.x_1_expected_mean.iloc[i]
+    std = np.sqrt(
+        1/(mean_hgf.attributes[0]["expected_precision"])
+    )
+
+    # the model expectations
+    ax.plot(x, norm(mean, std).pdf(x), color=color, label=i)
+
+
+# the sampling distribution
+ax.fill_between(x, norm(dist_mean, dist_std).pdf(x), color="#582766", alpha=.2)
+
+ax.legend(title="Iterations")
+ax.set_xlabel("Input (u)")
+ax.set_ylabel("Density")
+plt.grid(linestyle=":")
+sns.despine()
+```
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+## Kown mean, unknown precision
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+## Unkown mean, unknown precision
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
+dist_mean, dist_std = 5, 1
+input_data = np.random.normal(loc=dist_mean, scale=dist_std, size=1000)
+```
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
+mean_precision_hgf = (
+    HGF(model_type=None)
+    .add_input_node(kind="continuous", continuous_parameters={'continuous_precision': 0.01})
+    .add_value_parent(children_idxs=[0], tonic_volatility=-6.0)
+    .add_volatility_parent(children_idxs=[0], tonic_volatility=-6.0)
+    .init()
+).input_data(input_data)
+mean_precision_hgf.plot_network()
+```
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+tags: [hide-input]
+---
+# get the nodes trajectories
+df = mean_precision_hgf.to_pandas()
+
+fig, ax = plt.subplots(figsize=(12, 5))
+
+x = np.linspace(-10, 10, 1000)
+for i, color in zip(range(0, 150, 15), plt.cm.Greys(np.linspace(.2, 1, 10))):
+
+    # extract the sufficient statistics from the input node (and parents)
+    mean = df.x_1_expected_mean.iloc[i]
+    std = np.sqrt(
+        1/(mean_precision_hgf.attributes[0]["expected_precision"] * (1/np.exp(df.x_2_expected_mean.iloc[i])))
+)
+
+    # the model expectations
+    ax.plot(x, norm(mean, std).pdf(x), color=color, label=i)
+
+
+# the sampling distribution
+ax.fill_between(x, norm(dist_mean, dist_std).pdf(x), color="#582766", alpha=.2)
+
+ax.legend(title="Iterations")
+ax.set_xlabel("Input (u)")
+ax.set_ylabel("Density")
+plt.grid(linestyle=":")
+sns.despine()
+```
+
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
+## System configuration
+
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib
+```
diff --git a/docs/source/tutorials.md b/docs/source/tutorials.md
index fda03fea7..e1c1c9b47 100644
--- a/docs/source/tutorials.md
+++ b/docs/source/tutorials.md
@@ -33,6 +33,7 @@ glob:
 | Notebook | Colab |
 | --- | ---|
 | {ref}`example_1` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb)
+| {ref}`example_2` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb)
 
 ## Exercises
 
diff --git a/src/pyhgf/distribution.py b/src/pyhgf/distribution.py
index b3b65363d..e470d8e0a 100644
--- a/src/pyhgf/distribution.py
+++ b/src/pyhgf/distribution.py
@@ -31,7 +31,7 @@ def hgf_logp(
     volatility_coupling_1: Union[np.ndarray, ArrayLike, float] = 1.0,
     volatility_coupling_2: Union[np.ndarray, ArrayLike, float] = 1.0,
     input_data: List[np.ndarray] = [np.nan],
-    response_function: Callable = None,
+    response_function: Optional[Callable] = None,
     model_type: str = "continuous",
     n_levels: int = 2,
     response_function_parameters: List[Tuple] = [()],
diff --git a/src/pyhgf/model.py b/src/pyhgf/model.py
index 26ad7443d..a9ec9fb87 100644
--- a/src/pyhgf/model.py
+++ b/src/pyhgf/model.py
@@ -34,6 +34,10 @@ class HGF(object):
         network only has one input node.
     model_type :
         The model implemented (can be `"continuous"`, `"binary"` or `"custom"`).
+    update_type:
+        The type of volatility update to perform. Can be `"eHGF"` (default) or
+        `"standard"`. The eHGF update step tends to be more robust and produce fewer
+        invalid space errors and is therefore recommended by default.
     update_type :
         The type of update to perform for volatility coupling. Can be `"eHGF"`
         (defaults) or `"standard"`. The eHGF update step was proposed as an alternative
@@ -97,38 +101,42 @@ def __init__(
 
         Parameters
         ----------
-        eta0 :
-            The first categorical value of the binary node. Defaults to `0.0`. Only
-            relevant if `model_type="binary"`.
-        eta1 :
-            The second categorical value of the binary node. Defaults to `0.0`. Only
-            relevant if `model_type="binary"`.
+        n_levels :
+            The number of hierarchies in the perceptual model (can be `2` or `3`). If
+            `None`, the nodes hierarchy is not created and might be provided afterward.
+            Defaults to `2` for a 2-level HGF.
+        model_type : str
+            The model type to use (can be `"continuous"` or `"binary"`).
+        update_type:
+            The type of volatility update to perform. Can be `"eHGF"` (default) or
+            `"standard"`. The eHGF update step tends to be more robust and produce fewer
+            invalid space errors and is therefore recommended by default.
         initial_mean :
             A dictionary containing the initial values for the initial mean at
             different levels of the hierarchy. Defaults set to `0.0`.
         initial_precision :
             A dictionary containing the initial values for the initial precision at
             different levels of the hierarchy. Defaults set to `1.0`.
+        continuous_precision :
+            The expected precision of the continuous input node. Default to `1e4`. Only
+            relevant if `model_type="continuous"`.
+        tonic_volatility :
+            A dictionary containing the initial values for the tonic volatility
+            at different levels of the hierarchy. This represents the tonic
+            part of the variance (the part that is not affected by the parent node).
+            Defaults are set to `-3.0`.
         volatility_coupling :
             A dictionary containing the initial values for the volatility coupling
             at different levels of the hierarchy. This represents the phasic part of
             the variance (the part that is affected by the parent nodes) and will
             define the strength of the connection between the node and the parent
             node. Defaults set to `1.0`.
-        model_type : str
-            The model type to use (can be `"continuous"` or `"binary"`).
-        n_levels :
-            The number of hierarchies in the perceptual model (can be `2` or `3`). If
-            `None`, the nodes hierarchy is not created and might be provided afterward.
-            Defaults to `2` for a 2-level HGF.
-        tonic_volatility :
-            A dictionary containing the initial values for the tonic volatility
-            at different levels of the hierarchy. This represents the tonic
-            part of the variance (the part that is not affected by the parent node).
-            Defaults are set to `-3.0`.
-        continuous_precision :
-            The expected precision of the continuous input node. Default to `1e4`. Only
-            relevant if `model_type="continuous"`.
+        eta0 :
+            The first categorical value of the binary node. Defaults to `0.0`. Only
+            relevant if `model_type="binary"`.
+        eta1 :
+            The second categorical value of the binary node. Defaults to `0.0`. Only
+            relevant if `model_type="binary"`.
         binary_precision :
             The precision of the binary input node. Default to `jnp.inf`. Only relevant
             if `model_type="binary"`.
@@ -616,7 +624,7 @@ def add_value_parent(
         autoregressive_intercept: float = 0.0,
         additional_parameters: Optional[Dict] = None,
     ):
-        """Add a value parent to a given set of nodes.
+        r"""Add a value parent to a given set of nodes.
 
         Parameters
         ----------
@@ -727,7 +735,7 @@ def add_volatility_parent(
         self,
         children_idxs: Union[List, int],
         volatility_coupling: Union[float, np.ndarray, ArrayLike] = 1.0,
-        mean: Union[float, np.ndarray, ArrayLike] = 0.0,
+        mean: Union[float, np.ndarray, ArrayLike] = 1.0,
         precision: Union[float, np.ndarray, ArrayLike] = 1.0,
         tonic_volatility: Union[float, np.ndarray, ArrayLike] = -4.0,
         tonic_drift: Union[float, np.ndarray, ArrayLike] = 0.0,
@@ -735,7 +743,7 @@ def add_volatility_parent(
         autoregressive_intercept: float = 0.0,
         additional_parameters: Optional[Dict] = None,
     ):
-        """Add a volatility parent to a given set of nodes.
+        r"""Add a volatility parent to a given set of nodes.
 
         Parameters
         ----------
diff --git a/src/pyhgf/updates/binary.py b/src/pyhgf/updates/binary.py
index 57b649f0e..6e25d652a 100644
--- a/src/pyhgf/updates/binary.py
+++ b/src/pyhgf/updates/binary.py
@@ -7,17 +7,17 @@
 
 from pyhgf.typing import Edges
 from pyhgf.updates.prediction.binary import predict_binary_state_node
-from pyhgf.updates.prediction_error.binary import (
+from pyhgf.updates.prediction_error.inputs.binary import (
     prediction_error_input_value_parent,
-    prediction_error_value_parent,
 )
+from pyhgf.updates.prediction_error.nodes.binary import prediction_error_value_parent
 
 
 @partial(jit, static_argnames=("edges", "node_idx"))
 def binary_node_prediction_error(
     attributes: Dict, time_step: float, node_idx: int, edges: Edges, **args
 ) -> Dict:
-    """Update the value parent(s) of a binary node.
+    """Update the value parent(s) of a binary state node.
 
     In a three-level HGF, this step will update the node :math:`x_2`.
 
diff --git a/src/pyhgf/updates/continuous.py b/src/pyhgf/updates/continuous.py
index 83556eb72..fa47e6933 100644
--- a/src/pyhgf/updates/continuous.py
+++ b/src/pyhgf/updates/continuous.py
@@ -7,8 +7,13 @@
 
 from pyhgf.typing import Edges
 from pyhgf.updates.prediction.continuous import predict_mean, predict_precision
-from pyhgf.updates.prediction_error.continuous import (
+from pyhgf.updates.prediction_error.inputs.continuous import (
     prediction_error_input_mean_value_parent,
+    prediction_error_input_mean_volatility_parent,
+    prediction_error_input_precision_value_parent,
+    prediction_error_input_precision_volatility_parent,
+)
+from pyhgf.updates.prediction_error.nodes.continuous import (
     prediction_error_mean_value_parent,
     prediction_error_mean_volatility_parent,
     prediction_error_precision_value_parent,
@@ -129,7 +134,7 @@ def continuous_node_prediction_error(
 def ehgf_continuous_node_prediction_error(
     attributes: Dict, time_step: float, node_idx: int, edges: Edges, **args
 ) -> Dict:
-    """eHGF prediction-error step for value and volatility parents of a continuous node.
+    """Perform the eHGF PE step for value and volatility parents of a continuous node.
 
     This update step uses a different order for the mean and precision as compared to
     the standard HGF, respectively:
@@ -350,6 +355,7 @@ def continuous_input_prediction_error(
 
     # list value and volatility parents
     value_parents_idxs = edges[node_idx].value_parents
+    volatility_parents_idxs = edges[node_idx].volatility_parents
 
     ########################
     # Update value parents #
@@ -360,16 +366,53 @@ def continuous_input_prediction_error(
             # children will update the parent at once, otherwise just pass and wait
             if edges[value_parent_idx].value_children[-1] == node_idx:
                 # Estimate the new precision of the value parent
-                pi_value_parent = prediction_error_precision_value_parent(
+                precision_value_parent = prediction_error_input_precision_value_parent(
                     attributes, edges, value_parent_idx
                 )
+
                 # Estimate the new mean of the value parent
-                mu_value_parent = prediction_error_input_mean_value_parent(
-                    attributes, edges, value_parent_idx, pi_value_parent
+                mean_value_parent = prediction_error_input_mean_value_parent(
+                    attributes, edges, value_parent_idx, precision_value_parent
                 )
 
                 # update input node's parameters
-                attributes[value_parent_idx]["precision"] = pi_value_parent
-                attributes[value_parent_idx]["mean"] = mu_value_parent
+                attributes[value_parent_idx]["precision"] = precision_value_parent
+                attributes[value_parent_idx]["mean"] = mean_value_parent
+
+    #############################
+    # Update volatility parents #
+    #############################
+    if volatility_parents_idxs is not None:
+        for volatility_parent_idx in volatility_parents_idxs:
+            # if this child is the last one relative to this parent's family, all the
+            # children will update the parent at once, otherwise just pass and wait
+            if edges[volatility_parent_idx].volatility_children[-1] == node_idx:
+                # in the eHGF update step, we use the expected precision here
+                # as we haven't computed it yet due to the reverse update order
+                precision_volatility_parent = attributes[volatility_parent_idx][
+                    "expected_precision"
+                ]
+
+                # Estimate the new mean of the volatility parent
+                mean_volatility_parent = prediction_error_input_mean_volatility_parent(
+                    attributes,
+                    edges,
+                    time_step,
+                    volatility_parent_idx,
+                    precision_volatility_parent,
+                )
+                attributes[volatility_parent_idx]["mean"] = mean_volatility_parent
+
+                # Estimate the new precision of the volatility parent
+                precision_volatility_parent = (
+                    prediction_error_input_precision_volatility_parent(
+                        attributes, edges, time_step, volatility_parent_idx
+                    )
+                )
+
+                # Update this parent's parameters
+                attributes[volatility_parent_idx][
+                    "precision"
+                ] = precision_volatility_parent
 
     return attributes
diff --git a/src/pyhgf/updates/prediction/binary.py b/src/pyhgf/updates/prediction/binary.py
index d2c639908..0034f492c 100644
--- a/src/pyhgf/updates/prediction/binary.py
+++ b/src/pyhgf/updates/prediction/binary.py
@@ -28,18 +28,19 @@ def predict_binary_state_node(
         For each node, the index list value and volatility parents and children.
     time_step :
         The interval between the previous time point and the current time point.
-    value_parent_idx :
-        Pointer to the value parent node.
+    node_idx :
+        Pointer to the binary state node.
 
     Returns
     -------
-    pi_value_parent :
-        The precision (:math:`\\pi`) of the value parent.
-    mu_value_parent :
-        The mean (:math:`\\mu`) of the value parent.
+    expected_precision :
+        The precision of the value parent.
+    expected_mean :
+        The mean of the value parent.
+
     """
     # List the (unique) value parent of the value parent
-    value_parent_idx = edges[node_idx].value_parents[0]
+    value_parent_idx = edges[node_idx].value_parents[0]  # type: ignore
 
     # Estimate the new expected mean of the value parent and apply the sigmoid transform
     expected_mean = attributes[value_parent_idx]["expected_mean"]
diff --git a/src/pyhgf/updates/prediction/continuous.py b/src/pyhgf/updates/prediction/continuous.py
index 7d1876131..8c64db127 100644
--- a/src/pyhgf/updates/prediction/continuous.py
+++ b/src/pyhgf/updates/prediction/continuous.py
@@ -16,7 +16,7 @@ def predict_mean(
     time_step: float,
     node_idx: int,
 ) -> Array:
-    r"""Expected value for the mean of a probabilistic node.
+    r"""Compute the expected mean of a probabilistic node.
 
     Parameters
     ----------
@@ -75,7 +75,7 @@ def predict_mean(
 def predict_precision(
     attributes: Dict, edges: Edges, time_step: float, node_idx: int
 ) -> Array:
-    r"""Expected value for the precision of the value parent.
+    r"""Compute the expected precision of a probabilistic node.
 
     Parameters
     ----------
diff --git a/src/pyhgf/updates/prediction_error/inputs/__init__.py b/src/pyhgf/updates/prediction_error/inputs/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/pyhgf/updates/prediction_error/inputs/binary.py b/src/pyhgf/updates/prediction_error/inputs/binary.py
new file mode 100644
index 000000000..a33fdbb9e
--- /dev/null
+++ b/src/pyhgf/updates/prediction_error/inputs/binary.py
@@ -0,0 +1,102 @@
+# Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>
+
+from functools import partial
+from typing import Dict, Tuple
+
+import jax.numpy as jnp
+from jax import Array, jit
+from jax.lax import cond
+
+from pyhgf.math import binary_surprise, gaussian_density
+from pyhgf.typing import Edges
+
+
+@partial(jit, static_argnames=("edges", "value_parent_idx"))
+def prediction_error_input_value_parent(
+    attributes: Dict,
+    edges: Edges,
+    value_parent_idx: int,
+) -> Tuple[Array, ...]:
+    r"""Update the mean and precision of the value parent of a binary input node.
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
+        For each node, the index list value and volatility parents and children.
+    value_parent_idx :
+        Pointer to the value parent node that will be updated.
+
+    Returns
+    -------
+    pi_value_parent :
+        The precision (:math:`\\pi`) of the value parent.
+    mu_value_parent :
+        The mean (:math:`\\mu`) of the value parent.
+    surprise :
+        The binary surprise from observing the new state.
+
+    """
+    # Get the current expected mean for the value parent
+    # The prediction sequence was triggered by the new observation so this value is
+    # already in the node attributes
+    expected_mean_value_parent = attributes[value_parent_idx]["expected_mean"]
+
+    # Read parameters from the binary input
+    # Currently, only one binary input can be child of the binary node
+    child_node_idx = edges[value_parent_idx].value_children[0]  # type: ignore
+    eta0 = attributes[child_node_idx]["eta0"]
+    eta1 = attributes[child_node_idx]["eta1"]
+    expected_precision = attributes[child_node_idx]["expected_precision"]
+    value = attributes[child_node_idx]["value"]
+
+    # Compute the surprise, new mean and precision
+    mu_value_parent, pi_value_parent, surprise = cond(
+        expected_precision == jnp.inf,
+        input_surprise_inf,
+        input_surprise_reg,
+        (expected_precision, value, eta1, eta0, expected_mean_value_parent),
+    )
+
+    return pi_value_parent, mu_value_parent, surprise
+
+
+def input_surprise_inf(op):
+    """Apply special case if expected_precision is `jnp.inf`."""
+    _, value, _, _, expected_mean_value_parent = op
+    mu_value_parent = value
+    pi_value_parent = jnp.inf
+    surprise = binary_surprise(value, expected_mean_value_parent)
+
+    return mu_value_parent, pi_value_parent, surprise
+
+
+def input_surprise_reg(op):
+    """Compute the surprise, mu_value_parent and pi_value_parent."""
+    expected_precision, value, eta1, eta0, expected_mean_value_parent = op
+
+    # Likelihood under eta1
+    und1 = jnp.exp(-expected_precision / 2 * (value - eta1) ** 2)
+
+    # Likelihood under eta0
+    und0 = jnp.exp(-expected_precision / 2 * (value - eta0) ** 2)
+
+    # Eq. 39 in Mathys et al. (2014) (i.e., Bayes)
+    mu_value_parent = (
+        expected_mean_value_parent
+        * und1
+        / (expected_mean_value_parent * und1 + (1 - expected_mean_value_parent) * und0)
+    )
+    pi_value_parent = 1 / (mu_value_parent * (1 - mu_value_parent))
+
+    # Surprise
+    surprise = -jnp.log(
+        expected_mean_value_parent * gaussian_density(value, eta1, expected_precision)
+        + (1 - expected_mean_value_parent)
+        * gaussian_density(value, eta0, expected_precision)
+    )
+
+    return mu_value_parent, pi_value_parent, surprise
diff --git a/src/pyhgf/updates/prediction_error/inputs/continuous.py b/src/pyhgf/updates/prediction_error/inputs/continuous.py
new file mode 100644
index 000000000..c3a221386
--- /dev/null
+++ b/src/pyhgf/updates/prediction_error/inputs/continuous.py
@@ -0,0 +1,341 @@
+# Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>
+
+from functools import partial
+from typing import Dict
+
+import jax.numpy as jnp
+from jax import Array, jit
+from jax.typing import ArrayLike
+
+from pyhgf.typing import Edges
+
+
+@partial(jit, static_argnames=("edges", "value_parent_idx"))
+def prediction_error_input_precision_value_parent(
+    attributes: Dict, edges: Edges, value_parent_idx: int
+) -> Array:
+    r"""Send prediction-error and update the precision of a value parent (continuous).
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
+        For each node, the index list value and volatility parents and children.
+    value_parent_idx :
+        Pointer to the value parent node that will be updated.
+
+    Returns
+    -------
+    precision_value_parent :
+        The updated value for the precision of the value parent.
+
+    See Also
+    --------
+    prediction_error_mean_value_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
+
+    """
+    # Get the current expected mean for the volatility parent
+    # The prediction sequence was triggered by the new observation so this value is
+    # already in the node attributes
+    expected_precision_value_parent = attributes[value_parent_idx]["expected_precision"]
+
+    # Gather precision updates from all child nodes if the parent has many children.
+    # This part corresponds to the sum over children for the multi-children situations.
+    expected_precision_children = 0.0
+    for child_idx, value_coupling in zip(
+        edges[value_parent_idx].value_children,  # type: ignore
+        attributes[value_parent_idx]["value_coupling_children"],
+    ):
+        # the expected precision in the input node
+        expected_precision_child = attributes[child_idx]["expected_precision"]
+
+        # add the precision from volatility parents if any
+        if edges[child_idx].volatility_parents is not None:
+            volatility_coupling = attributes[edges[child_idx].volatility_parents[0]][
+                "volatility_coupling_children"
+            ][0]
+            expected_precision_child *= 1 / jnp.exp(
+                attributes[edges[child_idx].volatility_parents[0]]["expected_mean"]
+                * volatility_coupling
+            )
+
+        expected_precision_children += expected_precision_child * value_coupling**2
+
+    # Estimate new value for the precision of the value parent
+    precision_value_parent = (
+        expected_precision_value_parent + expected_precision_children
+    )
+    return precision_value_parent
+
+
+@partial(jit, static_argnames=("edges", "volatility_parent_idx"))
+def prediction_error_input_precision_volatility_parent(
+    attributes: Dict, edges: Edges, time_step: float, volatility_parent_idx: int
+) -> Array:
+    """Send prediction-error and update the precision of the volatility parent.
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
+        For each node, the index list value and volatility parents and children.
+    time_step :
+        The interval between the previous time point and the current time point.
+    volatility_parent_idx :
+        Pointer to the node that will be updated.
+
+    Returns
+    -------
+    precision_volatility_parent :
+        The new precision of the value parent.
+
+    See Also
+    --------
+    prediction_error_mean_volatility_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
+
+    """
+    # Get the current expected precision for the volatility parent
+    # The prediction sequence was triggered by the new observation so this value is
+    # already in the node attributes
+    expected_precision_volatility_parent = attributes[volatility_parent_idx][
+        "expected_precision"
+    ]
+
+    # gather volatility precisions from the child nodes
+    precision_volatility_children = 0.0
+    for child_idx, volatility_coupling in zip(
+        edges[volatility_parent_idx].volatility_children,  # type: ignore
+        attributes[volatility_parent_idx]["volatility_coupling_children"],
+    ):
+        # retireve the index of the value parent (assuming a unique value parent)
+        # we need this to compute the value PE, required for the volatility PE
+        this_value_parent_idx = edges[child_idx].value_parents[0]
+
+        # compute the expected precision from the input node
+        expected_precision_child = attributes[child_idx]["expected_precision"]
+
+        # add the precision from the volatility parent if any
+        expected_precision_child *= 1 / jnp.exp(
+            attributes[edges[child_idx].volatility_parents[0]]["expected_mean"]
+            * volatility_coupling
+        )
+
+        # compute the volatility prediction error for this input node
+        child_volatility_prediction_error = (
+            expected_precision_child / attributes[this_value_parent_idx]["precision"]
+            + expected_precision_child
+            * (
+                attributes[child_idx]["value"]
+                - attributes[this_value_parent_idx]["mean"]
+            )
+            ** 2
+            - 1
+        )
+
+        precision_volatility_children += (
+            0.5 * volatility_coupling**2 * (1 + child_volatility_prediction_error)
+        )
+
+    # Estimate the new precision of the volatility parent
+    precision_volatility_parent = (
+        expected_precision_volatility_parent + precision_volatility_children
+    )
+    precision_volatility_parent = jnp.where(
+        precision_volatility_parent <= 0, jnp.nan, precision_volatility_parent
+    )
+
+    return precision_volatility_parent
+
+
+@partial(jit, static_argnames=("edges", "volatility_parent_idx"))
+def prediction_error_input_mean_volatility_parent(
+    attributes: Dict,
+    edges: Edges,
+    time_step: float,
+    volatility_parent_idx: int,
+    precision_volatility_parent: ArrayLike,
+) -> Array:
+    r"""Send prediction-error and update the mean of the volatility parent.
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
+        For each node, the index list value and volatility parents and children.
+    time_step :
+        The interval between the previous time point and the current time point.
+    volatility_parent_idx :
+        Pointer to the node that will be updated.
+    precision_volatility_parent :
+        The precision of the volatility parent.
+
+    Returns
+    -------
+    mean_volatility_parent :
+        The updated value for the mean of the value parent (:math:`\\mu`).
+
+    See Also
+    --------
+    prediction_error_volatility_volatility_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
+
+    """
+    # Get the current expected mean for the volatility parent
+    # The prediction sequence was triggered by the new observation so this value is
+    # already in the node attributes
+    expected_mean_volatility_parent = attributes[volatility_parent_idx]["expected_mean"]
+
+    # Gather volatility prediction errors from the child nodes
+    children_volatility_prediction_error = 0.0
+    for child_idx, volatility_coupling in zip(
+        edges[volatility_parent_idx].volatility_children,  # type: ignore
+        attributes[volatility_parent_idx]["volatility_coupling_children"],
+    ):
+        # retireve the index of the value parent (assuming a unique value parent)
+        # we need this to compute the value PE, required for the volatility PE
+        this_value_parent_idx = edges[child_idx].value_parents[0]
+
+        # compute the expected precision from the input node
+        expected_precision_child = attributes[child_idx]["expected_precision"]
+
+        # add the precision from the volatility parent if anyu
+        expected_precision_child *= 1 / jnp.exp(
+            attributes[edges[child_idx].volatility_parents[0]]["expected_mean"]
+            * volatility_coupling
+        )
+
+        # compute the volatility prediction error for this input node
+        child_volatility_prediction_error = (
+            expected_precision_child / attributes[this_value_parent_idx]["precision"]
+            + expected_precision_child
+            * (
+                attributes[child_idx]["value"]
+                - attributes[this_value_parent_idx]["mean"]
+            )
+            ** 2
+            - 1
+        )
+
+        # sum over all input nodes
+        children_volatility_prediction_error += (
+            0.5
+            * child_volatility_prediction_error
+            * (volatility_coupling / attributes[volatility_parent_idx]["precision"])
+        )
+
+    # Estimate the new mean of the volatility parent
+    mean_volatility_parent = (
+        expected_mean_volatility_parent + children_volatility_prediction_error
+    )
+
+    return mean_volatility_parent
+
+
+@partial(jit, static_argnames=("edges", "value_parent_idx"))
+def prediction_error_input_mean_value_parent(
+    attributes: Dict,
+    edges: Edges,
+    value_parent_idx: int,
+    precision_value_parent: ArrayLike,
+) -> Array:
+    """Send value prediction-error to the mean of a continuous input's value parent.
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
+        For each node, the index list value and volatility parents and children.
+    value_parent_idx :
+        Pointer to the value parent node that will be updated.
+    precision_value_parent :
+        The precision of the value parent.
+
+    Returns
+    -------
+    mean_value_parent :
+        The new mean of the value parent.
+
+    Notes
+    -----
+    This update step is similar to the one used for the state node, except that it uses
+    the observed value instead of the mean of the child node, and the expected mean of
+    the parent node instead of the expected mean of the child node.
+
+    See Also
+    --------
+    prediction_error_mean_value_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
+
+    """
+    # gather PE updates from other input nodes
+    # in the case of a multivariate descendency
+    children_prediction_errors = 0.0
+    for child_idx, value_coupling in zip(
+        edges[value_parent_idx].value_children,  # type: ignore
+        attributes[value_parent_idx]["value_coupling_children"],
+    ):
+        # retireve the index of the value parent (assuming a unique value parent)
+        # we need this to compute the value PE, required for the volatility PE
+        this_value_parent_idx = edges[child_idx].value_parents[0]
+
+        child_value_prediction_error = (
+            attributes[child_idx]["value"]
+            - attributes[this_value_parent_idx]["expected_mean"]
+        )
+
+        expected_precision_child = attributes[child_idx]["expected_precision"]
+        if edges[child_idx].volatility_parents is not None:
+            volatility_coupling = attributes[edges[child_idx].volatility_parents[0]][
+                "volatility_coupling_children"
+            ][0]
+            expected_precision_child *= 1 / jnp.exp(
+                attributes[edges[child_idx].volatility_parents[0]]["expected_mean"]
+                * volatility_coupling
+            )
+
+        children_prediction_errors += (
+            (expected_precision_child / precision_value_parent)
+            * child_value_prediction_error
+            * value_coupling
+        )
+
+    # Compute the new mean of the value parent
+    mean_value_parent = (
+        attributes[value_parent_idx]["expected_mean"] + children_prediction_errors
+    )
+
+    return mean_value_parent
diff --git a/src/pyhgf/updates/prediction_error/nodes/__init__.py b/src/pyhgf/updates/prediction_error/nodes/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/pyhgf/updates/prediction_error/binary.py b/src/pyhgf/updates/prediction_error/nodes/binary.py
similarity index 52%
rename from src/pyhgf/updates/prediction_error/binary.py
rename to src/pyhgf/updates/prediction_error/nodes/binary.py
index b114480b3..6a8bf8282 100644
--- a/src/pyhgf/updates/prediction_error/binary.py
+++ b/src/pyhgf/updates/prediction_error/nodes/binary.py
@@ -3,12 +3,9 @@
 from functools import partial
 from typing import Dict, Tuple
 
-import jax.numpy as jnp
 from jax import Array, jit
-from jax.lax import cond
 from jax.typing import ArrayLike
 
-from pyhgf.math import binary_surprise, gaussian_density
 from pyhgf.typing import Edges
 
 
@@ -17,9 +14,9 @@ def prediction_error_mean_value_parent(
     attributes: Dict,
     edges: Edges,
     value_parent_idx: int,
-    pi_value_parent: ArrayLike,
+    precision_value_parent: ArrayLike,
 ) -> Array:
-    r"""Send prediction-error and update the mean of a value parent (binary).
+    """Send prediction-error and update the mean of a value parent (binary).
 
     .. note::
        This function has similarities with its continuous counterpart
@@ -37,13 +34,13 @@ def prediction_error_mean_value_parent(
         For each node, the index list value and volatility parents and children.
     value_parent_idx :
         Pointer to the node that will be updated.
-    pi_value_parent :
+    precision_value_parent :
         The precision of the value parent that has already been updated.
 
     Returns
     -------
-    mu_value_parent :
-        The expected value for the mean of the value parent (:math:`\\mu`).
+    mean_value_parent :
+        The expected value for the mean of the value parent.
 
     """
     # Get the current expected precision for the volatility parent
@@ -53,27 +50,29 @@ def prediction_error_mean_value_parent(
 
     # Gather prediction errors from all child nodes if the parent has many children
     # This part corresponds to the sum of children for the multi-children situations
-    pe_children = 0.0
-    for child_idx, psi_child in zip(
-        edges[value_parent_idx].value_children,
+    children_prediction_error = 0.0
+    for child_idx, value_coupling in zip(
+        edges[value_parent_idx].value_children,  # type: ignore
         attributes[value_parent_idx]["value_coupling_children"],
     ):
-        vape_child = (
+        child_value_prediction_error = (
             attributes[child_idx]["mean"] - attributes[child_idx]["expected_mean"]
         )
-        pe_children += (psi_child * vape_child) / pi_value_parent
+        children_prediction_error += (
+            value_coupling * child_value_prediction_error
+        ) / precision_value_parent
 
     # Estimate the new mean of the value parent
-    mu_value_parent = expected_mean_value_parent + pe_children
+    mean_value_parent = expected_mean_value_parent + children_prediction_error
 
-    return mu_value_parent
+    return mean_value_parent
 
 
 @partial(jit, static_argnames=("edges", "value_parent_idx"))
 def prediction_error_precision_value_parent(
     attributes: Dict, edges: Edges, value_parent_idx: int
 ) -> Array:
-    r"""Send prediction-error and update the precision of a value parent (binary).
+    """Send prediction-error and update the precision of a value parent (binary).
 
     .. note::
        This function has similarities with its continuous counterpart
@@ -94,8 +93,8 @@ def prediction_error_precision_value_parent(
 
     Returns
     -------
-    pi_value_parent :
-        The expected value for the mean of the value parent (:math:`\\pi`).
+    precision_value_parent :
+        The expected value for the mean of the value parent.
 
     """
     # Get the current expected precision for the volatility parent
@@ -107,16 +106,16 @@ def prediction_error_precision_value_parent(
     # This part corresponds to the sum over children for the multi-children situations.
     pi_children = 0.0
     for child_idx, psi_child in zip(
-        edges[value_parent_idx].value_children,
+        edges[value_parent_idx].value_children,  # type: ignore
         attributes[value_parent_idx]["value_coupling_children"],
     ):
         expected_precision_child = attributes[child_idx]["expected_precision"]
         pi_children += psi_child * (1 / expected_precision_child)
 
     # Estimate new value for the precision of the value parent
-    pi_value_parent = expected_precision_value_parent + pi_children
+    precision_value_parent = expected_precision_value_parent + pi_children
 
-    return pi_value_parent
+    return precision_value_parent
 
 
 @partial(jit, static_argnames=("edges", "value_parent_idx"))
@@ -129,9 +128,9 @@ def prediction_error_value_parent(
 
     Updating the posterior distribution of the value parent is a two-step process:
     #. Update the posterior precision using
-    :py:func:`pyhgf.updates.prediction_error.binary.prediction_error_precision_value_parent`.
+    :py:func:`pyhgf.updates.prediction_error.nodes.binary.prediction_error_precision_value_parent`.
     #. Update the posterior mean value using
-    :py:func:`pyhgf.updates.prediction_error.binary.prediction_error_mean_value_parent`.
+    :py:func:`pyhgf.updates.prediction_error.nodes.binary.prediction_error_mean_value_parent`.
 
     Parameters
     ----------
@@ -147,9 +146,9 @@ def prediction_error_value_parent(
     Returns
     -------
     pi_value_parent :
-        The precision (:math:`\\pi`) of the value parent.
+        The precision of the value parent.
     mu_value_parent :
-        The mean (:math:`\\mu`) of the value parent.
+        The mean of the value parent.
 
     """
     # Estimate the new precision of the value parent
@@ -162,94 +161,3 @@ def prediction_error_value_parent(
     )
 
     return pi_value_parent, mu_value_parent
-
-
-@partial(jit, static_argnames=("edges", "value_parent_idx"))
-def prediction_error_input_value_parent(
-    attributes: Dict,
-    edges: Edges,
-    value_parent_idx: int,
-) -> Tuple[Array, ...]:
-    r"""Update the mean and precision of the value parent of a binary input node.
-
-    Parameters
-    ----------
-    attributes :
-        The attributes of the probabilistic nodes.
-    edges :
-        The edges of the probabilistic nodes as a tuple of
-        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
-        For each node, the index list value and volatility parents and children.
-    value_parent_idx :
-        Pointer to the value parent node that will be updated.
-
-    Returns
-    -------
-    pi_value_parent :
-        The precision (:math:`\\pi`) of the value parent.
-    mu_value_parent :
-        The mean (:math:`\\mu`) of the value parent.
-    surprise :
-        The binary surprise from observing the new state.
-
-    """
-    # Get the current expected mean for the value parent
-    # The prediction sequence was triggered by the new observation so this value is
-    # already in the node attributes
-    expected_mean_value_parent = attributes[value_parent_idx]["expected_mean"]
-
-    # Read parameters from the binary input
-    # Currently, only one binary input can be child of the binary node
-    child_node_idx = edges[value_parent_idx].value_children[0]
-    eta0 = attributes[child_node_idx]["eta0"]
-    eta1 = attributes[child_node_idx]["eta1"]
-    expected_precision = attributes[child_node_idx]["expected_precision"]
-    value = attributes[child_node_idx]["value"]
-
-    # Compute the surprise, new mean and precision
-    mu_value_parent, pi_value_parent, surprise = cond(
-        expected_precision == jnp.inf,
-        input_surprise_inf,
-        input_surprise_reg,
-        (expected_precision, value, eta1, eta0, expected_mean_value_parent),
-    )
-
-    return pi_value_parent, mu_value_parent, surprise
-
-
-def input_surprise_inf(op):
-    """Apply special case if expected_precision is `jnp.inf`."""
-    _, value, _, _, expected_mean_value_parent = op
-    mu_value_parent = value
-    pi_value_parent = jnp.inf
-    surprise = binary_surprise(value, expected_mean_value_parent)
-
-    return mu_value_parent, pi_value_parent, surprise
-
-
-def input_surprise_reg(op):
-    """Compute the surprise, mu_value_parent and pi_value_parent."""
-    expected_precision, value, eta1, eta0, expected_mean_value_parent = op
-
-    # Likelihood under eta1
-    und1 = jnp.exp(-expected_precision / 2 * (value - eta1) ** 2)
-
-    # Likelihood under eta0
-    und0 = jnp.exp(-expected_precision / 2 * (value - eta0) ** 2)
-
-    # Eq. 39 in Mathys et al. (2014) (i.e., Bayes)
-    mu_value_parent = (
-        expected_mean_value_parent
-        * und1
-        / (expected_mean_value_parent * und1 + (1 - expected_mean_value_parent) * und0)
-    )
-    pi_value_parent = 1 / (mu_value_parent * (1 - mu_value_parent))
-
-    # Surprise
-    surprise = -jnp.log(
-        expected_mean_value_parent * gaussian_density(value, eta1, expected_precision)
-        + (1 - expected_mean_value_parent)
-        * gaussian_density(value, eta0, expected_precision)
-    )
-
-    return mu_value_parent, pi_value_parent, surprise
diff --git a/src/pyhgf/updates/prediction_error/continuous.py b/src/pyhgf/updates/prediction_error/nodes/continuous.py
similarity index 66%
rename from src/pyhgf/updates/prediction_error/continuous.py
rename to src/pyhgf/updates/prediction_error/nodes/continuous.py
index 293d8222f..ef376e29d 100644
--- a/src/pyhgf/updates/prediction_error/continuous.py
+++ b/src/pyhgf/updates/prediction_error/nodes/continuous.py
@@ -15,7 +15,7 @@ def prediction_error_mean_value_parent(
     attributes: Dict,
     edges: Edges,
     value_parent_idx: int,
-    pi_value_parent: ArrayLike,
+    precision_value_parent: ArrayLike,
 ) -> Array:
     r"""Send prediction-error and update the mean of a value parent (continuous).
 
@@ -29,14 +29,24 @@ def prediction_error_mean_value_parent(
         For each node, the index list value and volatility parents and children.
     value_parent_idx :
         Pointer to the value parent node that will be updated.
-    pi_value_parent :
-        The precision of the value parent that has already been updated.
+    precision_value_parent :
+        The precision of the value parent.
 
     Returns
     -------
-    mu_value_parent :
+    mean_value_parent :
         The updated value for the mean of the value parent (:math:`\\mu`).
 
+    See Also
+    --------
+    prediction_error_precision_value_parent, prediction_error_input_mean_value_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
+
     """
     # Get the current expected precision for the volatility parent
     # The prediction sequence was triggered by the new observation so this value is
@@ -45,23 +55,23 @@ def prediction_error_mean_value_parent(
 
     # Gather prediction errors from all child nodes if the parent has many children
     # This part corresponds to the sum of children for the multi-children situations
-    pe_children = 0.0
+    children_prediction_errors = 0.0
     for child_idx, value_coupling in zip(
-        edges[value_parent_idx].value_children,
+        edges[value_parent_idx].value_children,  # type: ignore
         attributes[value_parent_idx]["value_coupling_children"],
     ):
-        vape_child = (
+        child_value_prediction_error = (
             attributes[child_idx]["mean"] - attributes[child_idx]["expected_mean"]
         )
         expected_precision_child = attributes[child_idx]["expected_precision"]
-        pe_children += (
-            value_coupling * expected_precision_child * vape_child
-        ) / pi_value_parent
+        children_prediction_errors += (
+            value_coupling * expected_precision_child * child_value_prediction_error
+        ) / precision_value_parent
 
     # Estimate the new mean of the value parent
-    mu_value_parent = expected_mean_value_parent + pe_children
+    mean_value_parent = expected_mean_value_parent + children_prediction_errors
 
-    return mu_value_parent
+    return mean_value_parent
 
 
 @partial(jit, static_argnames=("edges", "value_parent_idx"))
@@ -86,6 +96,16 @@ def prediction_error_precision_value_parent(
     pi_value_parent :
         The updated value for the precision of the value parent (:math:`\\pi`).
 
+    See Also
+    --------
+    prediction_error_mean_value_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
+
     """
     # Get the current expected mean for the volatility parent
     # The prediction sequence was triggered by the new observation so this value is
@@ -94,25 +114,25 @@ def prediction_error_precision_value_parent(
 
     # Gather precision updates from all child nodes if the parent has many children.
     # This part corresponds to the sum over children for the multi-children situations.
-    pi_children = 0.0
+    children_precisions = 0.0
     for child_idx, value_coupling in zip(
-        edges[value_parent_idx].value_children,
+        edges[value_parent_idx].value_children,  # type: ignore
         attributes[value_parent_idx]["value_coupling_children"],
     ):
         expected_precision_child = attributes[child_idx]["expected_precision"]
-        pi_children += value_coupling**2 * expected_precision_child
+        children_precisions += value_coupling**2 * expected_precision_child
 
     # Estimate new value for the precision of the value parent
-    pi_value_parent = expected_precision_value_parent + pi_children
+    precision_value_parent = expected_precision_value_parent + children_precisions
 
-    return pi_value_parent
+    return precision_value_parent
 
 
 @partial(jit, static_argnames=("edges", "volatility_parent_idx"))
 def prediction_error_precision_volatility_parent(
     attributes: Dict, edges: Edges, time_step: float, volatility_parent_idx: int
 ) -> Array:
-    r"""Send prediction-error and update the precision of the volatility parent.
+    """Send prediction-error and update the precision of the volatility parent.
 
     Parameters
     ----------
@@ -124,13 +144,23 @@ def prediction_error_precision_volatility_parent(
         For each node, the index list value and volatility parents and children.
     time_step :
         The interval between the previous time point and the current time point.
-    value_parent_idx :
+    volatility_parent_idx :
         Pointer to the node that will be updated.
 
     Returns
     -------
-    pi_volatility_parent :
-        The updated value for the mean of the value parent (:math:`\\pi`).
+    precision_volatility_parent :
+        The new precision of the value parent.
+
+    See Also
+    --------
+    prediction_error_mean_volatility_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
 
     """
     # Get the current expected precision for the volatility parent
@@ -143,24 +173,25 @@ def prediction_error_precision_volatility_parent(
     # gather volatility precisions from the child nodes
     children_volatility_precision = 0.0
     for child_idx, kappas_children in zip(
-        edges[volatility_parent_idx].volatility_children,
+        edges[volatility_parent_idx].volatility_children,  # type: ignore
         attributes[volatility_parent_idx]["volatility_coupling_children"],
     ):
         # Look at the (optional) volatility parents and update logvol accordingly
         logvol = attributes[child_idx]["tonic_volatility"]
         if edges[child_idx].volatility_parents is not None:
-            for chld_vo_pa, volatility_coupling in zip(
+            for children_volatility_parents, volatility_coupling in zip(
                 edges[child_idx].volatility_parents,
                 attributes[child_idx]["volatility_coupling_parents"],
             ):
-                logvol += volatility_coupling * attributes[chld_vo_pa]["mean"]
+                logvol += (
+                    volatility_coupling
+                    * attributes[children_volatility_parents]["mean"]
+                )
 
         # Compute new value for nu
         nu_children = time_step * jnp.exp(logvol)
         nu_children = jnp.where(nu_children > 1e-128, nu_children, jnp.nan)
 
-        expected_precision_children = attributes[child_idx]["expected_precision"]
-        pi_children = attributes[child_idx]["precision"]
         vope_children = (
             1 / attributes[child_idx]["precision"]
             + (attributes[child_idx]["mean"] - attributes[child_idx]["expected_mean"])
@@ -169,26 +200,39 @@ def prediction_error_precision_volatility_parent(
 
         children_volatility_precision += (
             0.5
-            * (kappas_children * nu_children * expected_precision_children) ** 2
-            * (1 + (1 - 1 / (nu_children * pi_children)) * vope_children)
+            * (
+                kappas_children
+                * nu_children
+                * attributes[child_idx]["expected_precision"]
+            )
+            ** 2
+            * (
+                1
+                + (1 - 1 / (nu_children * attributes[child_idx]["precision"]))
+                * vope_children
+            )
         )
 
     # Estimate the new precision of the volatility parent
-    pi_volatility_parent = (
+    precision_volatility_parent = (
         expected_precision_volatility_parent + children_volatility_precision
     )
-    pi_volatility_parent = jnp.where(
-        pi_volatility_parent <= 0, jnp.nan, pi_volatility_parent
+    precision_volatility_parent = jnp.where(
+        precision_volatility_parent <= 0, jnp.nan, precision_volatility_parent
     )
 
-    return pi_volatility_parent
+    return precision_volatility_parent
 
 
 @partial(jit, static_argnames=("edges", "volatility_parent_idx"))
 def prediction_error_mean_volatility_parent(
-    attributes, edges, time_step, volatility_parent_idx, pi_volatility_parent: ArrayLike
+    attributes: Dict,
+    edges: Edges,
+    time_step: float,
+    volatility_parent_idx: int,
+    precision_volatility_parent: ArrayLike,
 ) -> Array:
-    r"""Send prediction-error and update the mean of the volatility parent.
+    """Send prediction-error and update the mean of the volatility parent.
 
     Parameters
     ----------
@@ -200,13 +244,25 @@ def prediction_error_mean_volatility_parent(
         For each node, the index list value and volatility parents and children.
     time_step :
         The interval between the previous time point and the current time point.
-    valueolatility_parent_idx :
+    volatility_parent_idx :
         Pointer to the node that will be updated.
+    precision_volatility_parent :
+        The precision of the volatility parent.
 
     Returns
     -------
-    mu_volatility_parent :
-        The updated value for the mean of the value parent (:math:`\\mu`).
+    mean_volatility_parent :
+        The updated value for the mean of the value parent.
+
+    See Also
+    --------
+    prediction_error_volatility_volatility_parent
+
+    References
+    ----------
+    .. [1] Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., &
+       Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
+       arXiv. https://doi.org/10.48550/ARXIV.2305.10937
 
     """
     # Get the current expected mean for the volatility parent
@@ -217,23 +273,25 @@ def prediction_error_mean_volatility_parent(
     # Gather volatility prediction errors from the child nodes
     children_volatility_prediction_error = 0.0
     for child_idx, kappas_children in zip(
-        edges[volatility_parent_idx].volatility_children,
+        edges[volatility_parent_idx].volatility_children,  # type: ignore
         attributes[volatility_parent_idx]["volatility_coupling_children"],
     ):
         # Look at the (optional) volatility parents and update logvol accordingly
         logvol = attributes[child_idx]["tonic_volatility"]
         if edges[child_idx].volatility_parents is not None:
-            for chld_vo_pa, volatility_coupling in zip(
+            for children_volatility_parents, volatility_coupling in zip(
                 edges[child_idx].volatility_parents,
                 attributes[child_idx]["volatility_coupling_parents"],
             ):
-                logvol += volatility_coupling * attributes[chld_vo_pa]["mean"]
+                logvol += (
+                    volatility_coupling
+                    * attributes[children_volatility_parents]["mean"]
+                )
 
         # Compute new value for nu
         nu_children = time_step * jnp.exp(logvol)
         nu_children = jnp.where(nu_children > 1e-128, nu_children, jnp.nan)
 
-        expected_precision_children = attributes[child_idx]["expected_precision"]
         vope_children = (
             1 / attributes[child_idx]["precision"]
             + (attributes[child_idx]["mean"] - attributes[child_idx]["expected_mean"])
@@ -243,66 +301,14 @@ def prediction_error_mean_volatility_parent(
             0.5
             * kappas_children
             * nu_children
-            * expected_precision_children
-            / pi_volatility_parent
+            * attributes[child_idx]["expected_precision"]
+            / precision_volatility_parent
             * vope_children
         )
 
     # Estimate the new mean of the volatility parent
-    mu_volatility_parent = (
+    mean_volatility_parent = (
         expected_mean_volatility_parent + children_volatility_prediction_error
     )
 
-    return mu_volatility_parent
-
-
-@partial(jit, static_argnames=("edges", "value_parent_idx"))
-def prediction_error_input_mean_value_parent(
-    attributes: Dict,
-    edges: Edges,
-    value_parent_idx: int,
-    pi_value_parent: ArrayLike,
-) -> Array:
-    r"""Send prediction-error to the mean of a continuous input's value parent.
-
-    Parameters
-    ----------
-    attributes :
-        The attributes of the probabilistic nodes.
-    edges :
-        The edges of the probabilistic nodes as a tuple of
-        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
-        For each node, the index list value and volatility parents and children.
-    value_parent_idx :
-        Pointer to the value parent node that will be updated.
-
-    Returns
-    -------
-    mu_value_parent :
-        The updated value for the mean of the value parent (:math:`\\mu`).
-
-    """
-    # Get the current expected mean for the volatility parent.
-    # The prediction sequence was triggered by the new observation so this value is
-    # already in the node attributes.
-    expected_mean_value_parent = attributes[value_parent_idx]["expected_mean"]
-
-    # gather PE updates from other input nodes
-    # in the case of a multivariate descendency
-    pe_children = 0.0
-    for child_idx, value_coupling in zip(
-        edges[value_parent_idx].value_children,
-        attributes[value_parent_idx]["value_coupling_children"],
-    ):
-        child_value_prediction_error = (
-            attributes[child_idx]["value"] - expected_mean_value_parent
-        )
-        expected_precision_child = attributes[child_idx]["expected_precision"]
-        pe_children += (
-            value_coupling * expected_precision_child * child_value_prediction_error
-        ) / pi_value_parent
-
-    # Compute the new mean of the value parent
-    mu_value_parent = expected_mean_value_parent + pe_children
-
-    return mu_value_parent
+    return mean_volatility_parent