diff --git a/Exercise sheet 2/exercise_sheet_02.ipynb b/Exercise sheet 2/exercise_sheet_02.ipynb
index 6e3fcbc..f65d6fd 100644
--- a/Exercise sheet 2/exercise_sheet_02.ipynb	
+++ b/Exercise sheet 2/exercise_sheet_02.ipynb	
@@ -565,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "82fe6efd",
    "metadata": {
     "deletable": false,
@@ -580,20 +580,45 @@
      "task": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAc8UlEQVR4nO3df3BU9f3v8ecbBFMpqEVpO4ASWi2NJN3aEAFb2RawqVW5TuuAsa21VyNt1TpTvVK/lrlfy1dxdPrDH/fSndbqbd3Kd/xVxlL1azVQrRSC3UaIpaWAJVoav1gxIDEi7/vHJsvJsiGbZDdnc/J6zDjm7DnZfXsM73x4n/fn8zF3R0REhr4RYQcgIiKFoYQuIhIRSugiIhGhhC4iEhFK6CIiEXFUWB98wgkn+JQpU8L6eBGRIWnjxo3/7e4n5joXWkKfMmUKjY2NYX28iMiQZGYv93ROJRcRkYhQQhcRiQgldBGRiAithi4i0fDOO+/Q0tJCe3t72KFESllZGZMmTWLUqFF5f48SuogMSEtLC2PHjmXKlCmYWdjhRIK7s3v3blpaWigvL8/7+1RyEZEBaW9vZ/z48UrmBWRmjB8/vs9/61FCF5EBUzIvvP7cUyV0EZGIUEIXEemHVCrF6tWr+/x98Xi8aJMq9VBUBJiy5Nfdjncs/3xIkchQkUqlaGxs5Jxzzgk7lAyN0EUkEn7xi19QU1NDLBbjiiuu4A9/+ANVVVW0t7ezb98+TjvtNDZt2kRDQwNnnXUWF1xwARUVFSxevJiDBw8C8OSTTzJr1ixOP/10LrzwQvbu3QvAhg0bmD17Nh/72Meoqalhz549LF26lJUrVxKLxVi5ciX79u3ja1/7GjNmzODjH/84v/rVrwDYv38/ixYtoqqqioULF7J///6i3QON0EWkYK655hpSqVRB3zMWi/HDH/7wiNe89NJLrFy5kueee45Ro0bxjW98gy1btnD++edz4403sn//fr70pS8xffp0GhoaWL9+Pc3NzZx88snU1tby8MMPE4/HWbZsGU899RRjxozh1ltv5fvf/z5Llixh4cKFrFy5khkzZvDmm29yzDHHcNNNN9HY2Mhdd90FwA033MBnPvMZ7rnnHt544w1qamqYN28eP/7xjznmmGNoamqiqamJ008/vaD3J0gJXUSGvN/+9rds3LiRGTNmAOlR8YQJE1i6dCkzZsygrKyMO+64I3N9TU0NU6dOBeCiiy7i2WefpaysjObmZs4880wAOjo6mDVrFlu2bOGDH/xg5r3HjRuXM4Ynn3ySVatWcfvttwPpds6///3vrF27lquvvhqAqqoqqqqqinMTUEIXkQLqbSRdLO7OJZdcwi233NLt9V27drF3717eeecd2tvbGTNmDHB4S6CZ4e7Mnz+fX/7yl93ONTU15dVC6O489NBDfOQjHzns3GC1deZVQzezWjPbYmZbzWxJjvNxM9tjZqnOf5YWPlQRkdzmzp3Lgw8+SGtrKwCvv/46L7/8MvX19Xzve9/j4osv5vrrr89cv379erZv387BgwdZuXIln/zkJ5k5cybPPfccW7duBeCtt97iL3/5C9OmTePVV19lw4YNALS1tXHgwAHGjh1LW1tb5j0/+9nPcuedd+LuAPzxj38E4KyzzuL+++8HYNOmTTQ1NRXtPvQ6QjezkcDdwHygBdhgZqvcvTnr0t+5+7lFiFFE5IgqKipYtmwZZ599NgcPHmTUqFEsWLCAo446irq6Ot59911mz57N008/zYgRI5g1axZLlizhxRdfzDwgHTFiBPfeey8XXXQRb7/9NgDLli3j1FNPZeXKlVx11VXs37+f97znPTz11FN8+tOfZvny5cRiMb7zne/w3e9+l2uuuYaqqircnSlTpvDYY4/x9a9/nUsvvZSqqipisRg1NTVFuw/W9dukxwvMZgH/290/23n8HQB3vyVwTRy4ti8Jvbq62rXBhZQKtS3230svvcRHP/rRsMPIW0NDA7fffjuPPfZY2KH0Kte9NbON7l6d6/p8Si4TgZ2B45bO17LNMrM/mdlvzOy0XG9kZvVm1mhmja+99loeHy0iIvnKJ6HnquZnD+tfAE52948BdwKP5nojd0+4e7W7V594Ys4t8UREiioejw+J0Xl/5JPQW4DJgeNJwKvBC9z9TXff2/n1amCUmZ1QsChFRKRX+ST0DcApZlZuZqOBRcCq4AVm9gHr7Msxs5rO991d6GBFRKRnvXa5uPsBM7sSeAIYCdzj7pvNbHHn+RXAF4Gvm9kBYD+wyHt72ioiIgWV18SizjLK6qzXVgS+vgu4q7ChiYhIX2imqIgUVHYL6EDl00K6Y8cOzj33XDZt2tTt9aVLl3LWWWcxb968nN/36KOPcuqpp1JRUVGQWMOm1RZFJLJuuummHpM5pBN6c3P2HMn+OXDgQEHeZyCU0EUkEt59910uv/xyTjvtNM4++2z279/PV7/6VR588EEAlixZQkVFBVVVVVx77bX8/ve/Z9WqVVx33XXEYjH+9re/kUqlmDlzJlVVVVxwwQX861//AtLL51ZVVTFr1iyuu+46pk+fDsC9997LhRdeyHnnncfZZ5/N3r17mTt3LqeffjqVlZWZJXR37NjBtGnTuOyyy5g+fToXX3wxTz31FGeeeSannHIK69evL8g9UEIXkUj461//yje/+U02b97Mcccdx0MPPZQ59/rrr/PII4+wefNmmpqauPHGG5k9ezbnn38+t912G6lUig996EN85Stf4dZbb6WpqYnKykr+/d//HYBLL72UFStW8PzzzzNy5Mhun/v8889z33338fTTT1NWVsYjjzzCCy+8wDPPPMO3v/3tzNouW7du5Vvf+hZNTU38+c9/JplM8uyzz3L77bdz8803F+QeKKGLSCSUl5cTi8UA+MQnPsGOHTsy58aNG0dZWRmXXXYZDz/8MMccc8xh379nzx7eeOMN5syZA8All1zC2rVreeONN2hra2P27NkA1NXVdfu++fPn8773vQ9Ir7h4ww03UFVVxbx583jllVf45z//mYmvsrKSESNGcNpppzF37lzMjMrKym6xDoQSuohEwtFHH535euTIkd1q2kcddRTr16/nC1/4Ao8++ii1tbV5v29vHdhdS/IC3H///bz22mts3LiRVCrF+9//ftrb2w+Lb8SIEZnjESNGFKz+ri4XiZxgl8WROiQK3Y0hpWvv3r289dZbnHPOOcycOZMPf/jDAN2WwD322GM5/vjj+d3vfsenPvUpfv7znzNnzhyOP/54xo4dy7p165g5cyYPPPBAj5+zZ88eJkyYwKhRo3jmmWd4+eWXB+W/r4sSuogUVCmuVNnW1saCBQtob2/H3fnBD34AwKJFi7j88su54447ePDBB7nvvvtYvHgxb731FlOnTuVnP/sZAD/96U+5/PLLGTNmDPF4nGOPPTbn51x88cWcd955VFdXE4vFmDZt2qD9N0Iey+cWi5bPlWIpxAi9FJNSqRpqy+f2x969e3nve98LwPLly/nHP/7Bj370o6J/bl+Xz9UIXUSkF7/+9a+55ZZbOHDgACeffDL33ntv2CHlpIQuItKLhQsXsnDhwrDD6JW6XERkwLQWX+H1554qoYvIgJSVlbF7924l9QJyd3bv3k1ZWVmfvk8lFxEZkEmTJtHS0oK2lSyssrIyJk2a1KfvUUIXkQEZNWoU5eXlYYchqOQiIhIZSugiIhGhhC4iEhGqoUskFHNdlnxnnoqETSN0EZGIUEIXEYkIJXQRkYhQQhcRiQgldBGRiFBCFxGJCCV0EZGIUB+6RFp2f7r6yCXKNEIXEYkIJXQRkYhQQhcRiYi8ErqZ1ZrZFjPbamZLjnDdDDN718y+WLgQRUQkH70mdDMbCdwNfA6oAC4ys4oerrsVeKLQQYqISO/yGaHXAFvdfZu7dwAPAAtyXHcV8BDQWsD4REQkT/m0LU4EdgaOW4AzgheY2UTgAuAzwIyCRScyCNpSj7OvuaHba/F1tx06X1bJ2FjtIEcl0nf5jNAtx2vZ23v/ELje3d894huZ1ZtZo5k1akNZKRX7mhvoaN2e81wqlTos2YuUqnxG6C3A5MDxJODVrGuqgQfMDOAE4BwzO+DujwYvcvcEkACorq7O/qUgMmgSiQS7kncC0NG6ndETyvlA3fLM+YbOCUjxeJy16xrZlUz3AiSmvkJ9ff3gByySh3xG6BuAU8ys3MxGA4uAVcEL3L3c3ae4+xTgQeAb2clcpJQkk8nMqHz0hHLGVMRzXldXV8foCekd7Ttat5NMJgcrRJE+63WE7u4HzOxK0t0rI4F73H2zmS3uPL+iyDGKFEX2qDyX+vp6bt42ESAzShcpVXmt5eLuq4HVWa/lTOTu/tWBhyVSeG2px4nH0w87U6kUjJt85G/IIZVKEY/HM8d1dXUqwUjJ0ExRGTb2NTekEzkQi8V6LLP0ZExFnFgsljlOpVIqwUhJ0WqLMqzEYjEaGhqAw1di7M3YWC0Ny+/MHAdH6iKlQCN0EZGI0AhdIit7wlBH63aYOr6gnxGsqaueLmFTQpfI6pow1NV2OHpCOXV1dQV7/+B7ddXmldAlTEroEmnZrYn19YXbsai+vj6TwFVPl1KghC7SB9rSTkqZHoqKiESEErqISESo5CKREuxsCT4QHQzqeJGwKaFLpAQ7W3ItutXXyUT5UseLlAIldImcfBbdKjR1vEgpUA1dRCQilNBFRCJCJReRHIpVaxcpJo3QRUQiQiN0GfJy7Q8aNm2EIWFQQpchr2t/0J5aFQdb9gJgamOUwaKELpEQRqtitkN194ns6NxEA9TGKINHNXQRkYhQQhcRiQgldBGRiFBCFxkE67btZsqSX6u/XYpKCV1EJCLU5SJDTiKRIJlMZo5TqRSMmxxeQHnoaN3OruQSABJTX1ELoxSFRugy5CSTyUxvN0AsFgu99/xI6urqMpOdOlq3d/tlJFJIGqHLkBSLxWgI9HqXcm26vr6em7dNBMiM0kWKQSN0EZGIUEIXEYkIJXQRkYhQDV1kAEq5di/DT14jdDOrNbMtZrbVzA57qmNmC8ysycxSZtZoZp8sfKgiInIkvY7QzWwkcDcwH2gBNpjZKndvDlz2W2CVu7uZVQH/CUwrRsAiIpJbPiP0GmCru29z9w7gAWBB8AJ33+vu3nk4BnBECiiRSBCPx4nH49160Ieirs0v4vE4iUQi7HAkQvKpoU8EdgaOW4Azsi8yswuAW4AJwOdzvZGZ1QP1ACeddFJfY5VhLJlMsnZdY3qCzrjJh20iMVSMqYhT2f4ioI0vpPDySeiW47XDRuDu/gjwiJmdBXwPmJfjmgSQAKiurtYoXvokuIlFfX3OMUPJGxurpWF5ers8bXwhhZZPyaUFCC6UMQl4taeL3X0t8CEzO2GAsYmISB/kk9A3AKeYWbmZjQYWAauCF5jZh83MOr8+HRgN7C50sCIi0rNeSy7ufsDMrgSeAEYC97j7ZjNb3Hl+BfAF4Ctm9g6wH1gYeEgqUnDq/xY5XF4Ti9x9NbA667UVga9vBW4tbGgiItIXmvovIhIRSugiIhGhtVykpHTVxttSj2f6tWFo7EoUlG+Nf+26RspOqswc33HjVepLl37TCF1K0r7mhiG1K1F/BHcyAu1mJAOnEbqUrKG0K1F/BHcyAu1mJAOnEbqISEQooYuIRIQSuohIRCihi4hEhBK6iEhEKKGLiESEErqISESoD11KRiKRYFcyvflDR+t2mDo+5IgGX9f2dJCeeKRZo9IXGqFLyUgmk+lETnp3oqG6zVx/jamIE4vFgHRi16xR6SuN0KWkBLeZu3kb3Byx2aFHou3pZKA0QhcRiQgldBGRiFBCFxGJCNXQZdBlr5q4Y/nnQ4pEJFo0QhcRiQgldBGRiFDJRULVlnqcePw2YOhtM1dsmmQkfaWELqHa19zA2tbt6a3Yxk2O3DZzueSz81JwUlXXVnxK6NIbJXQJXXAykaTV19dnErgmGUm+VEMXEYkIJXQRkYhQQhcRiQgldBGRiFBCFxGJCCV0EZGIUNuiyBAU7GXXWjjSJa+Ebma1wI+AkcBP3H151vmLges7D/cCX3f3PxUyUImG4DZzkN5qbvSE8hAjGhqCs0YB2soqGRurDS8gKUm9JnQzGwncDcwHWoANZrbK3ZsDl20H5rj7v8zsc0ACOKMYAcvQ1rXNXFcSHz2hfFjMDh2I7K34UqkU7eN2K6HLYfIZodcAW919G4CZPQAsADIJ3d1/H7h+HTCpkEFKtGhmaN8EZ41Ceuboum27Q4xISlU+D0UnAjsDxy2dr/XkfwK/yXXCzOrNrNHMGl977bX8oxQRkV7lk9Atx2ue80KzT5NO6NfnOu/uCXevdvfqE088Mf8oRUSkV/mUXFqA4Jqmk4BXsy8ysyrgJ8Dn3F1/HxQRGWT5jNA3AKeYWbmZjQYWAauCF5jZScDDwJfd/S+FD1NERHrT6wjd3Q+Y2ZXAE6TbFu9x981mtrjz/ApgKTAe+D9mBnDA3auLF7YMNV1907v0MK/g1JMuXfLqQ3f31cDqrNdWBL6+DLissKGJiEhfaKaoFF1wMpEmEhVGR+t2diWXADCmIq6edAG0losMgq7JRKCJRIVQV1eX+aXY0bqdfc0N4QYkJUMjdCmY7L0yg/VcTSbKT0/7jQbvZX19PTdvS08F6Rqli4BG6CIikaGELiISEUroIiIRoRq6FI16z0UGl0boIiIRoYQuIhIRKrlIUbSlHs/0R2syUXEFJxklpr7Sbe10GV6U0KUo9jU3ZBK5JhMVT/C+drRu5+pld2Z61EFruww3SuhSNJpMVHxjY7WZaf+aZCSqoYuIRIQSuohIRCihi4hEhBK6iEhEKKGLiESEulykIIKbWIB6z8MS7EkH9aUPNxqhS0EEN7EAbWQRhjEV8W6/RDtat5NMJkOMSAabRuhSMOo7D1ewJx3Ulz4caYQuIhIRSugiIhGhkovIENDTXqMiQRqhi4hEhBK6iEhEKKFLvyUSCeLxOPF4nFQqFXY4kkMqlcr8P0okEmGHI0WmGrr0WzKZZO26xnTv87jJ6jsvMWMq4uxrbmDdtt2ZOQLBSUbBurzWTY8GJXQZEPWely6tlT78qOQiIhIRSugiIhGhhC4iEhF5JXQzqzWzLWa21cwOK8aZ2TQze97M3jazawsfpoiI9KbXh6JmNhK4G5gPtAAbzGyVuzcHLnsduBr4H8UIUkpHIpHIrOCXSqVg3ORwA5K8dbUwdmkrq+y2mJcMffmM0GuAre6+zd07gAeABcEL3L3V3TcA7xQhRikhyWQy03Mei8XUqjhEjKmIE4vFMsepVIp9zQ2hxSPFkU/b4kRgZ+C4BTijPx9mZvVAPcBJJ53Un7eQEtA+bjI7Zl4HwNiQY5H8jI3V0rD80AYk8Xicddt2hxiRFEM+I3TL8Zr358PcPeHu1e5efeKJJ/bnLUREpAf5JPQWIFgonQS8WpxwRESkv/JJ6BuAU8ys3MxGA4uAVcUNS0RE+qrXGrq7HzCzK4EngJHAPe6+2cwWd55fYWYfABqBccBBM7sGqHD3N4sXugyGYFcLqLMlSoIbSmsz6WjIqw/d3Ve7+6nu/iF3/4/O11a4+4rOr3e5+yR3H+fux3V+rWQeAcGuFlBnS1TU1dVlNpTWZtLRocW5pFexWIyGhobMsXbPGfrq6+u5edtEQAt3RYmm/ouIRIRG6JKh9bGjTX+zij6N0EVEIkIJXQ7TlnpcW8sNM9qqLhqU0OUw+5obuq3XUldXF25AUlTBdV5SqZQ6XoYw1dCHsSPVVLM7WyS6guu8BFdjlKFHI3QRkYhQQhcRiQiVXARIPwjtWh+7o3U7TB0fbkASmuyNMOrq6rQswBChhC5A+kFoR+t2Rk8oZ/SE8m4PQtW/PHxkPwDvejieK6Fn/1xo7kL4lNAlY/SEcj5QtxyA+nr94RyO6uvruyVvPSQdWlRDFxGJCI3Qh6lEIsGu5KEtybrKLSLZgjV11dNLmxL6MNNV99yVvLNbEh89oVzL4sph6urqWLdtN+u27U4/LCd3PV1KgxL6MBasmYvkomV2hxbV0EVEIkIj9GEkWDdXzVz6I1hPbyurZGysNtyApBsl9GEkmUx26zVXzVy65DPXYExFnMr2F4F0Ym8ft1sJvcQooQ8zqptLf2Uv4rVu2+6QI5JsSugRl0gkMsuhplIpGDc53IAkMjpat3d7UJqY+oo6YEKmh6IRl0wmu61trjKLFEJdXV23ZzAdrdu1jnoJ0Ah9GAiuba51WaQQgu2MUJiWRq0NM3BK6BETLLFAuszStRuNSDFpRmn4VHKJmGCJBbSFnAwObWNXGjRCj4DsB5/aPk4GW3YHjEbr4VBCH0J6qjF2jcpjsRixWIwXyyq7XatapBTKkZ7BdJ1rK6ukq8qXaz11/WwWjxL6ENWWepx4/Dbg8FG5HnxKmI40WgfNMC0mJfQhInu527d3bmINMGfOHNXJpWRl/1yuWbMGWJPZ7lC964WlhF7CgrXx9B8EOHry9My/77jxKv1hkJKWvQNSIpHg6mXpgcnbOzdxxRVXZH7GNXIfOCX0QZJvGSS4WfPbOzcB6VH4nDlzeDHrBz64TVyhyywq20gxBPvXu37W123b3fmzfmjkDjA+9XjOBK+6e8/ySuhmVgv8CBgJ/MTdl2edt87z5wBvAV919xcKHGtkBJN2tq4kfvTk6YeNwpVkJUrGxmozCTv7z8TbOzfx9s5NOf+cqEzTs14TupmNBO4G5gMtwAYzW+XuzYHLPgec0vnPGcD/7fx3pGVP4jmSXYGFjIJJO9vRk6czpiKe+UHXZs0yHASTO/Q86Mku0/TFcGifzGeEXgNsdfdtAGb2ALAACCb0BcD/c3cH1pnZcWb2QXf/R09vumXLliG/o3hXXXvOnDl9+r7spC0i3WUn+C5tqcczS/j2xZo1a1izZk3kJzxZOgcf4QKzLwK17n5Z5/GXgTPc/crANY8By9392c7j3wLXu3tj1nvVA12/Ij8CbCnUf8gAnAD8d9hBlAjdi0N0Lw7RvTikFO7Fye5+Yq4T+YzQLcdr2b8F8rkGd08AiTw+c9CYWaO7V4cdRynQvThE9+IQ3YtDSv1e5LOWSwsQXER7EvBqP64REZEiyiehbwBOMbNyMxsNLAJWZV2zCviKpc0E9hypfi4iIoXXa8nF3Q+Y2ZXAE6TbFu9x981mtrjz/ApgNemWxa2k2xYvLV7IBVdSJaCQ6V4contxiO7FISV9L3p9KCoiIkOD1kMXEYkIJXQRkYhQQu9kZteamZvZCWHHEhYzu83M/mxmTWb2iJkdF3ZMg83Mas1si5ltNbOBb5Q5RJnZZDN7xsxeMrPNZvatsGMKm5mNNLM/ds67KUlK6KR/eEkvbfD3sGMJ2X8B0929CvgL8J2Q4xlUgWUuPgdUABeZWUW4UYXmAPBtd/8oMBP45jC+F12+BbwUdhBHooSe9gPgf5FjMtRw4u5PuvuBzsN1pOcTDCeZZS7cvQPoWuZi2HH3f3QtsOfubaQT2cRwowqPmU0CPg/8JOxYjmTYJ3QzOx94xd3/FHYsJeZrwG/CDmKQTQR2Bo5bGMZJrIuZTQE+Dvwh5FDC9EPSg76DIcdxRMNiPXQzewr4QI5T/wbcAJw9uBGF50j3wt1/1XnNv5H+K/f9gxlbCchrCYvhxMzeCzwEXOPub4YdTxjM7Fyg1d03mlk85HCOaFgkdHefl+t1M6sEyoE/pZd0ZxLwgpnVuPuuQQxx0PR0L7qY2SXAucBcH36TFLSERYCZjSKdzO9394fDjidEZwLnm9k5QBkwzsx+4e5fCjmuw2hiUYCZ7QCq3T3s1dRC0bmRyfeBOe7+WtjxDDYzO4r0w+C5wCukl72oc/fNoQYWgs5Na+4DXnf3a0IOp2R0jtCvdfdzQw4lp2FfQ5du7gLGAv9lZikzWxF2QIOp84Fw1zIXLwH/ORyTeaczgS8Dn+n8WUh1jlClhGmELiISERqhi4hEhBK6iEhEKKGLiESEErqISEQooYuIRIQSuohIRCihi4hExP8H0iUfIFEdSf8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def sample_Zn(alpha,b,n):\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    assert alpha > 2\n",
+    "    assert b > 0\n",
+    "    assert n >= 1 and type(n) == int\n",
+    "    \n",
+    "    # TODO: Remove ternaries as we already assert.\n",
+    "    E_X = alpha*b/(alpha - 1) if alpha > 1 else np.inf\n",
+    "    Var_X = alpha*b**2/( (alpha - 1)**2*(alpha - 2) ) if alpha > 2 else np.inf\n",
+    "    \n",
+    "    inv_pareto_samples = [inversion_sample(lambda p: f_inv_pareto(alpha, b, p)) for _ in range(n)]\n",
+    "    return np.sqrt(n/Var_X)*(np.mean(inv_pareto_samples) - E_X)\n",
     "\n",
     "# Plotting\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "alpha = 4\n",
+    "b = 1\n",
+    "n = 1000\n",
+    "pdf = gaussian\n",
+    "samples = [inversion_sample(lambda p: sample_Zn(alpha, b, n)) for _ in range(1000)]\n",
+    "compare_plot(samples, pdf, -5, 5, 100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "b5360d77",
    "metadata": {
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