diff --git a/notebooks/Model Comparison/Daten/dummy.csv b/notebooks/Model Comparison/Daten/dummy.csv
new file mode 100644
index 0000000000000000000000000000000000000000..54408cf28396d4fcd52ad3bea737bccba2ccb9b2
--- /dev/null
+++ b/notebooks/Model Comparison/Daten/dummy.csv	
@@ -0,0 +1,33 @@
+-1.081 9.357
+-0.888 8.322
+-0.623 8.745
+-0.480 7.346
+-0.505 7.028
+-0.345 4.032
+0.052 1.490
+0.061 3.265
+0.215 4.525
+0.395 3.339
+0.643 2.045
+0.871 -1.336
+0.776 1.264
+0.833 -0.813
+1.224 -0.335
+1.300 0.152
+1.563 -1.797
+1.669 -4.772
+1.967 -3.043
+1.950 -5.293
+2.030 -4.401
+2.284 -5.294
+2.534 -2.912
+2.531 -3.680
+2.765 -3.975
+2.913 -7.202
+3.168 -4.317
+3.119 -7.854
+3.385 -5.167
+3.488 -5.135
+3.538 -6.754
+3.900 -6.662
+3.970 -6.080
diff --git a/notebooks/Model Comparison/MC_3_1.ipynb b/notebooks/Model Comparison/MC_3_1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a94fd8885a66af5016a35daecfc8195e65390f21
--- /dev/null
+++ b/notebooks/Model Comparison/MC_3_1.ipynb	
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2bc64f1b-8f55-40b3-bfe2-38e144d482f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pymc as pm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3677ec51-54fd-48cd-b379-ce7161c0c1f9",
+   "metadata": {},
+   "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>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>55.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>53.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>50.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>52.05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0\n",
+       "0  51.06\n",
+       "1  55.12\n",
+       "2  53.73\n",
+       "3  50.24\n",
+       "4  52.05"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"./Daten/chemical_shifts.csv\",header=None)\n",
+    "#df = df.iloc[:,0]\n",
+    "\n",
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2db6d34d-e1f4-4e8b-b001-fac7c7ab8910",
+   "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, σ]\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\">/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 3 seconds.\n",
+      "Sampling: [y]\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\">/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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, σ, ν]\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\">/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 4 seconds.\n",
+      "Sampling: [y]\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\">/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/opt/conda/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with pm.Model() as model_g:\n",
+    "    μ = pm.Uniform('μ', lower=40, upper=70)\n",
+    "    σ = pm.HalfNormal('σ', sigma=10)\n",
+    "    y = pm.Normal('y', mu=μ, sigma=σ, observed=df)\n",
+    "    trace_g = pm.sample(2000, idata_kwargs={\"log_likelihood\": True}, random_seed=4591)\n",
+    "    trace_g.extend(pm.sample_posterior_predictive(trace_g, random_seed=4591))\n",
+    "    \n",
+    "with pm.Model() as model_t:\n",
+    "    μ = pm.Uniform('μ', 40, 75)\n",
+    "    σ = pm.HalfNormal('σ', sigma=10)\n",
+    "    ν = pm.Exponential('ν', 1/30)\n",
+    "    y = pm.StudentT('y', mu=μ, sigma=σ, nu=ν, observed=df)\n",
+    "    trace_t = pm.sample(2000, idata_kwargs={\"log_likelihood\": True}, random_seed=4591)\n",
+    "    trace_t.extend(pm.sample_posterior_predictive(trace_t, random_seed=4591))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "0e05f801-5548-4533-9c46-7ffc290582e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/arviz/stats/stats.py:792: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.70 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/arviz/stats/stats.py:792: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.70 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "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>rank</th>\n",
+       "      <th>elpd_loo</th>\n",
+       "      <th>p_loo</th>\n",
+       "      <th>elpd_diff</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>se</th>\n",
+       "      <th>dse</th>\n",
+       "      <th>warning</th>\n",
+       "      <th>scale</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>model_t</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-122.479620</td>\n",
+       "      <td>3.940166</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>9.110407</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>log</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_g</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-131.694721</td>\n",
+       "      <td>5.685827</td>\n",
+       "      <td>9.215101</td>\n",
+       "      <td>1.243450e-14</td>\n",
+       "      <td>12.510035</td>\n",
+       "      <td>5.110462</td>\n",
+       "      <td>True</td>\n",
+       "      <td>log</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         rank    elpd_loo     p_loo  elpd_diff        weight         se  \\\n",
+       "model_t     0 -122.479620  3.940166   0.000000  1.000000e+00   9.110407   \n",
+       "model_g     1 -131.694721  5.685827   9.215101  1.243450e-14  12.510035   \n",
+       "\n",
+       "              dse  warning scale  \n",
+       "model_t  0.000000     True   log  \n",
+       "model_g  5.110462     True   log  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp_df = az.compare({\"model_g\": trace_g, \"model_t\": trace_t})\n",
+    "# cmp_df.to_markdown()\n",
+    "cmp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "82618723-2f74-4c98-8a0e-011f9889cd6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Model comparison\\nhigher is better'}, xlabel='elpd_loo (log)', ylabel='ranked models'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_compare(cmp_df)"
+   ]
+  }
+ ],
+ "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": 5
+}