diff --git a/notebooks/Normal and t-Distribution/BIND_2_5.ipynb b/notebooks/Normal and t-Distribution/BIND_2_5.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e2364e926a30f7d8f60bf6d2fd46ebcb1490262c
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+++ b/notebooks/Normal and t-Distribution/BIND_2_5.ipynb	
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.stats as st\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import warnings \n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "import arviz as az\n",
+    "import pymc as pm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vielleicht haben wir alle aufgrund unserer Erfahrung das Gefühl, dass bei Ehepaaren (oder bei Paaren allgemein) der Ehemann (oder Mann) älter ist als die Ehefrau (oder Frau). Es gibt eine Studie aus Grossbritanien, die diese Behauptung untersucht. Im Datensatz `husband_wife.csv` sind die Daten von 170 Ehepaaren aufgeführt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age.husband</th>\n",
+       "      <th>height.husband</th>\n",
+       "      <th>age.wife</th>\n",
+       "      <th>height.wife</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>49</td>\n",
+       "      <td>180</td>\n",
+       "      <td>43</td>\n",
+       "      <td>159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25</td>\n",
+       "      <td>184</td>\n",
+       "      <td>28</td>\n",
+       "      <td>156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40</td>\n",
+       "      <td>165</td>\n",
+       "      <td>30</td>\n",
+       "      <td>162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>52</td>\n",
+       "      <td>177</td>\n",
+       "      <td>57</td>\n",
+       "      <td>154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>58</td>\n",
+       "      <td>161</td>\n",
+       "      <td>52</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age.husband  height.husband  age.wife  height.wife\n",
+       "0           49             180        43          159\n",
+       "1           25             184        28          156\n",
+       "2           40             165        30          162\n",
+       "3           52             177        57          154\n",
+       "4           58             161        52          142"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"./Daten/husband_wife.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Für jedes Ehepaar gibt es zwei Messungen: das Alter der Ehefrau und das Alter des Ehemannes. Wir interessieren uns für den Altersunterschied. In diesem Fall haben wir ja für jedes Testobjekt, nämlich für jedes Paar, zwei Messgrössen, nämlich das Alter der Frau und das Alter des Mannes. In diesem Fall ist es naheliegend, die `Differenz` des Alters zu betrachten. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     6\n",
+       "1    -3\n",
+       "2    10\n",
+       "3    -5\n",
+       "4     6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff = data[\"age.husband\"] - data[\"age.wife\"]\n",
+    "diff.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dies hat den Vorteil, dass wir nicht zwei Posterior-Verteilungen betrachten müssen, sondern nur noch eine. \n",
+    "\n",
+    "Zuerst wollen wir den Q-Q-Plot der Differenzen in der Abbildung unten betrachten. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Differenz Alter')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "st.probplot(diff,plot=plt)\n",
+    "plt.title(\"Differenz Alter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir können in diesem Fall von normalverteilten Altersdifferenzen ausgehen, obwohl am unteren und am oberen Ende die Datenpunkte ein bisschen ausreissen. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir wählen für die Likelihood-Funktion wiederum die Normalverteilung. Als Prior-Verteilung wählen wir auch für diesen Fall eine gleichförmige Verteilung, was unser Unwissen in Bezug auf die Altersdifferenzen ausdrückt.  Wir wählen eine Gleichverteilung im Bereich $ [0, 10] $, d.h., wir gehen also davon aus, dass der mittlere Unterschied im Bereich von $ 0$ bis $ 10 $ Jahren zwischen Ehemännern und Ehefrauen liegt, wobei alle Werte gleich wahrscheinlich sind. Warum wählen wir auch nicht negative Werte von $ \\mu $? Nun wir drücken damit unser Vorwissen, resp. unsere Erfahrung aus, dass Ehemänner tendenziell älter sind als ihre Ehefrauen.\n",
+    "\n",
+    "Da die Standardabweichung schwierig zu schätzen ist, nehmen wir als Standardabweichung die geschätzte Standardabweichung der Daten `diff.std()`. Somit erhalten wir das folgende `PyMC`-Modell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [μ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'μ'}>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with pm.Model() as model_paired:\n",
+    "    μ = pm.Uniform('μ', lower=-10, upper=10)\n",
+    "    σ = diff.std()\n",
+    "    y = pm.Normal('y', mu=μ, sigma=σ, observed=diff)\n",
+    "    trace_paired = pm.sample(1000)\n",
+    "\n",
+    "az.plot_posterior(trace_paired)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Somit liegt der Mittelwert der Posterior-Verteilung bei $ \\mu=2.2 $ und 94\\% der wahrscheinlichsten Werte für $ \\mu $ liegen im Bereich $ [1.7,2.8] $. \n",
+    "\n",
+    "Nun stellt sich noch die Frage, ob es einen statistisch relevanten Unterschied beim Altersunterschied gibt. Nehmen wir einmal an, dass es keinen Altersunterschied gibt, also $ \\mu=0 $. Wie lautet Ihre Test-Entscheidung?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Savage-Dickey Verhältnis Plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oft möchten wir eine Null-Hypothese $H_0$ (oder _Null-Modell_) mit einer Alternativ-Hypothese $H_1$ vergleichen. Im Beispiel des Altersunterschieds zwischen Ehemännern und Ehefrauen wollen wir die Nullhypothese $\\mu_0=0$ (kein Unterschied in Bezug auf Alter zwischen Ehemännern und Ehefrauen) mit der Alternativhypothese $\\mu\\neq 0$ vergleichen. In diesem Fall ist die Nullhypothese $\\mu_0=0$ in die Alternativhypothese ($\\mu\\neq 0$) eingebettet. D.h. der Nullwert ($\\mu_0=0$) ist ein Spezialfall des Modells der Alternativhypothese, das wir eruieren möchten. \n",
+    "\n",
+    "In diesem Fall können wir den sogenannten \\emph{Bayes-Faktor} sehr einfach berechnen - wir besprechen den Bayes-Faktor später. Der Bayes-Faktor hat in diesem Fall eine intuitive Form: Wir vergleichen den Wert der Prior-Verteilung mit der Posterior-Verteilung an der Stelle des Nullwertes $\\mu_0$ unter der Alternativhypothese:\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\text{BF}_{01}=\\frac{p(x\\mid H_0)}{p(x\\mid H_1)}= \\frac{p(\\mu=\\mu_0 \\mid x, H_1)}{p(\\mu=\\mu_0\\mid H_1)}\\equiv\\frac{\\text{Wert der Posterior-Dichte am Nullwert}}{\\text{Wert der Prior-Dichte am Nullwert}}\n",
+    "\\end{equation*}\n",
+    "\n",
+    "Diese Beziehung für den Bayes-Faktor gilt nur, wenn $H_0$ ein Spezialfall von $H_1$ ist. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [μ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n",
+      "Sampling: [y, μ]\n",
+      "The reference value is outside of the posterior. This translate into infinite support for H1, which is most likely an overstatement.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "({'BF10': 7.7022598539738, 'BF01': 0.12983202578968736},\n",
+       " <Axes: title={'center': 'The BF_10 is 7.70\\nThe BF_01 is 0.13'}, xlabel='μ', ylabel='Density'>)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with pm.Model() as model_h:\n",
+    "    μ = pm.Uniform('μ', lower=0, upper=10)\n",
+    "    σ = diff.std()\n",
+    "    y = pm.Normal('y', mu=μ, sigma=σ, observed=diff)\n",
+    "    trace_h = pm.sample(1000)\n",
+    "    trace_h.extend(pm.sample_prior_predictive(8000))\n",
+    "az.plot_bf(trace_h, var_name=\"μ\", ref_val=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [μ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n",
+      "Sampling: [y, μ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "({'BF10': 0.20072838426921713, 'BF01': 4.9818564705766715},\n",
+       " <Axes: title={'center': 'The BF_10 is 0.20\\nThe BF_01 is 4.98'}, xlabel='μ', ylabel='Density'>)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with pm.Model() as model_h:\n",
+    "    μ = pm.Normal('μ', sigma=2, mu=2)\n",
+    "    σ = diff.std()\n",
+    "    y = pm.Normal('y', mu=μ, sigma=σ, observed=diff)\n",
+    "    trace_h = pm.sample(1000)\n",
+    "    trace_h.extend(pm.sample_prior_predictive(8000))\n",
+    "az.plot_bf(trace_h, var_name=\"μ\", ref_val=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}