diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 95bfcb9..52fb66a 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,13 +11,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -31,10 +32,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def bayes(priori, verosimilitud): \n",
+    "    marginal=sum(np.multiply(priori, verosimilitud))\n",
+    "    posteriori=np.divide(np.multiply(priori, verosimilitud), marginal)\n",
+    "    return posteriori"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#p(tazon1/vainilla)=[p(vainilla/tazon1)*p(tazon1)]/p(vainilla)#\n",
+    "#p(vainilla/tazon1)= 30/40#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(vainilla)=1/2 *30/40+1/2*20/40\n",
+    "pv1=30/40\n",
+    "p1=1/2\n",
+    "pv=1/2*30/40+1/2*20/40\n",
+    "\n",
+    "\n",
+    "p_v_1=(pv1*p1)/pv\n",
+    "p_v_1"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -45,10 +82,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#p(tazon1/vainilla)=[p(vainilla/tazon2)*p(tazon2)]/p(vainilla)#\n",
+    "#p(vainilla/tazon1)= 30/40#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(vainilla)=1/2 *30/40+1/2*20/40\n",
+    "\n",
+    "pv2=20/40\n",
+    "p2=1/2\n",
+    "pv=1/2*30/40+1/2*20/40\n",
+    "\n",
+    "p_v_2=(pv2*p2)/pv\n",
+    "p_v_2"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -59,10 +120,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3333333333333333"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#p(tazon1/chocolaate)=[p(chocolate/tazon1)*p(tazon1)]/p(chocolate)#\n",
+    "#p(chocolate/tazon1)= 30/40#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(chocolate)=1/2 *30/40+1/2*20/40\n",
+    "\n",
+    "pv1=10/40\n",
+    "p1=1/2\n",
+    "pv=1/2*10/40+1/2*20/40\n",
+    "\n",
+    "p_c_1=(pv1*p1)/pv\n",
+    "p_c_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6666666666666666"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#p(tazon1/chocolate)=[p(chocolate/tazon2)*p(tazon2)]/p(chocolate)#\n",
+    "#p(chocolate/tazon1)= 30/40#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(tazon1)=1/2#\n",
+    "#p(chocolate)=1/2 *30/40+1/2*20/40\n",
+    "\n",
+    "pv2=20/40\n",
+    "p2=1/2\n",
+    "pv=1/2*10/40+1/2*20/40\n",
+    "\n",
+    "p_c_2=(pv2*p2)/pv\n",
+    "p_c_2"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -95,10 +211,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bag 1 given yellow:  0.5882352941176471\n"
+     ]
+    }
+   ],
+   "source": [
+    "#p(bolsa1/amarillo)=[p(amarillo/bolsa1)*p(bolsa1)]/p(amarillo)#\n",
+    "#p(amarillo/bolsa1)= 0,2#\n",
+    "#p(bolsa1)=1/2#\n",
+    "#p(amarillo)=1/2 *0,2+1/2*0,14\n",
+    "\n",
+    "py1=2/10\n",
+    "p1=1/2\n",
+    "py=(1/2*2/10)+(1/2*14/100)\n",
+    "\n",
+    "p_y_1=(py1*p1)/py\n",
+    "p_y_1\n",
+    "\n",
+    "print('Bag 1 given yellow: ', p_y_1)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -109,10 +247,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bag 2 given yellow:  0.411764705882353\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#p(bolsa2/amarillo)=[p(amarillo/bolsa2)*p(bolsa2)]/p(amarillo)#\n",
+    "#p(amarillo/bolsa2)= 0,2#\n",
+    "#p(bolsa2)=1/2#\n",
+    "#p(amarillo)=1/2 *0,2+1/2*0,14\n",
+    "\n",
+    "py2=14/100\n",
+    "p2=1/2\n",
+    "py=(1/2*2/10)+(1/2*14/100)\n",
+    "\n",
+    "p_y_2=(py2*p2)/py\n",
+    "\n",
+    "\n",
+    "print('Bag 2 given yellow: ', p_y_2)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -123,10 +284,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bag 1 given Green:  0.3333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "#p(bolsa1/verde)=[p(verde/bolsa1)*p(bolsa1)]/p(verde)#\n",
+    "#p(verde/bolsa1)= 0,1#\n",
+    "#p(bolsa1)=1/2#\n",
+    "#p(verde)=(1/2*1/10)+(1/2*2/10)#\n",
+    "\n",
+    "pg1=1/10\n",
+    "p1=1/2\n",
+    "pg=(1/2*1/10)+(1/2*2/10)\n",
+    "\n",
+    "p_g_1=(pg1*p1)/pg\n",
+    "p_g_1\n",
+    "\n",
+    "print('Bag 1 given Green: ', p_g_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bag 2 given Green:  0.6666666666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "#p(bolsa1/verde)=[p(verde/bolsa1)*p(bolsa1)]/p(verde)#\n",
+    "#p(verde/bolsa1)= 0,2#\n",
+    "#p(bolsa1)=1/2#\n",
+    "#p(verde)=(1/2*1/10)+(1/2*2/10)#\n",
+    "\n",
+    "pg2=2/10\n",
+    "p2=1/2\n",
+    "pg=(1/2*1/10)+(1/2*2/10)\n",
+    "\n",
+    "p_g_2=(pg2*p2)/pg\n",
+    "\n",
+    "\n",
+    "print('Bag 2 given Green: ', p_g_2)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -141,10 +353,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3333333333333333"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#p(/puerta B)=[p(verde/bolsa1)*p(bolsa1)]/p(verde)#\n",
+    "#p(verde/bolsa1)= 1/2#\n",
+    "#p(bolsa1)=1/3#\n",
+    "#p(verde)=(1/3*1/2)+(1/3*0)+(1/3*1)\n",
+    "\n",
+    "pg2=1/2\n",
+    "p2=1/3\n",
+    "pg=(1/3*1/2)+(1/3*0)+(1/3*1)\n",
+    "\n",
+    "p_g_2=(pg2*p2)/pg\n",
+    "p_g_2"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -157,10 +392,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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26PmYLdRdthERWLurzuooSo2LFoUYc6i5m7r2Pj0+wWL+7laW5mfw4h4dV1DRRYtCjNlQERhPOEuLguUuXpDNzpp2alp1LiQVPbQoxJh3KpvwJDuZPT3F6ihxb/WCbABe2q1dSCp6aFGIAXn5BYgIIsLj67ZwaMs6bDbb0LKRLir0ir0plHiTWbtbu5BU9HBYHUBN3uHqQ9yxdh/tPQP8fv1BLjzjFBZ/5QsnXeeW1aXhCRfnVi/M4TevV9DWPcA0d4LVcZQa1ZhaCiLyoYlcRlqmrHWs73pmepLFSdQxFy/Ixuc3rNtXb3UUpcZkrN1Hvx7jMmWhwy09uBw2slKcVkdRQYtnpeNNdfGijiuoKHHS7iMRWQGsBLwicsuwu9IAeyiDqfGrae1hZnqSjhlEEJtNuGh+Ns9uraF3YJDEBP23UZFttJaCE0ghUDxSh13agWsm+qQiki4iT4jIXhHZIyIrRCRTRF4UkQPBnxkT/f3xqLPPR1vPADMztOso0qxemE1X/yBvlzdZHUWpUZ20pWCMeQ14TUQeMMZUTeHz3gX83RhzjYg4ATfwL8DLxpjbRWQNsAa4dQqfM6bVtOh4QqRaWeIh2Wln7e46zp833eo4Sp3UWMcUXCJyn4isFZFXjl0m8oQiMg1YBdwPYIzpN8a0AlcADwYf9iBw5UR+f7w63NqN027Dm+qyOoo6jsth57zS6by0p07PsaAi3lh3Sf0TcC/wW2Bwks9ZBDQAvxeR04DNwDeBbGNMbfAxR4HskVYWkZuAmwDy8/MnGSV21LT0MCM9EZuOJ0Sk1QuzeX5HLVsPB6a/UCpSjbWl4DPG3GOM2WiM2XzsMsHndABLgXuMMUuALgJdRUNMYL7hEb9SGWPuM8YsM8Ys83q9E4wQW2zudFq6dTwhkp1XOh2HTXSCPBXxxloU/iIiXxGR3OCAcKaITHRyncPAYWPMhuDtJwgUiToRyQUI/tQdu8coMXiS+FnpOlV2pJqWlMBZxR5e1KObVYQba1G4HvgusJ5Ad89mYNNEntAYcxSoFpFjh9ReCOwGng0+z7Hne2Yivz8eufJOJcEuOp4Q4S5ekE15QxflDZ1WR1HqhMY0pmCMKZri5/068FBwz6MK4IsECtTjInIjUAVcO8XPGbMS808hd1oSdpuOJ0Syixdkc9uzu3hxdx0l5+qEhSoyjakoiMjnR1pujPnDRJ7UGLMVWDbCXRdO5PfFs+aufpzeQh1PiAIz0pM4ZWYaa3cd5eZzS6yOo9SIxtp9dMawyznAj4FPhCiTGoeNwfMxz9LjE6LC6gU5vFfdSn1Hr9VRlBrRWLuPvj78toikA4+GIpAan42VzfgH+piepuMJEUVsI043kuAtZMYN/0XJOVfSue2FD9w3Ky+f6kNTeYyoUuM30amzuwgcb6AstqGyif4je3HYTrE6ihrO+Llj7b4PLzaGB9YfpPAzt3LFL371gft0OnMVCcY6pvAX3j9uwA7MBx4PVSg1Nm3dA+yubae3eieTmIpKhZGIUOxNYUdNG/0+P06HnudKRZaxthT+Y9h1H1BljDkcgjxqHN6uaMIY6D24zeooahxKvMlsrW6lqqmLOdmpVsdR6gPG9DUlODHeXgIzpGYA/aEMpcZmfXkjbqedvtr9VkdR4zBjWhKJDhsVjV1WR1HqQ8Z65rVrgY3APxI4fmCDiGh/hcXWlzdxRmEm+H1WR1HjYLMJRd5kKhu7GNQJ8lSEGWuH5veBM4wx1xtjPg+cCfwwdLHUaOraeymr7+Ts2R6ro6gJKPGm0OfzcyR4ClWlIsVYi4LNGDN8LqKmcayrQmB9eSMAK0uyLE6iJiI/043dJjrlhYo4Y/1g/7uIvCAiXxCRLwDPA38NXSw1mvVlTaS7E1iQm2Z1FDUBCXYbBZluKhq7CEwKrFRkGO0czbMJnOfguyJyFfCR4F1vAw+FOpwamTGG9eVNrCj2YNP5jqJWsTeZisYu6jv6yE5LtDqOUsDoLYVfEjgfM8aYp4wxtxhjbgGeDt6nLHCouZua1h5Wluh4QjQr8aZgEzhQp11IKnKMVhSyjTE7jl8YXFYYkkRqVG+VBU4Av3K2jidEs8QEO/mZbvbXd2gXkooYoxWF9JPcpzOwWeSt8kay01wUZyVbHUVNUml2Kh29PmrbdII8FRlGKwqbRORLxy8UkX8icKIdFWZ+v+Gd8ibOLskaccI1FV2KvMnYbcL+ug6roygFjD7NxbeAp0XkM7xfBJYBTuAfQphLncDu2naauvq16yhGuBx2ijzJ7K/rBNG9vJX1TloUjDF1wEoROR84Ng3n88aYV0KeTI3o9QMNAKyao0UhVszNTqGsoZPE/FOtjqLUmM+nsA5YF+Isagxe39/AvJxUpusujDGjKCuZBLvgnr/K6ihK6VHJ0aSrz8fmqhbOneu1OoqaQg67jRJvCu65K+n3+a2Oo+KcFoUo8k5FEwODhlVaFGLO3OxU7EmpvLqvfvQHKxVCWhSiyOv7G0hKsLOsMMPqKGqKFWS68XU28/gmPU2JspYWhSjy+oFGzirOxOWwWx1FTTGbTeja+Qrr9tVT36HHLCjraFGIEtXN3VQ2dmnXUQzr3PEig37D01tqrI6i4pgWhSjx2v7grqhaFGKWr7mGZQUZPL6pWqe9UJbRohAlXt/fwMz0JJ3aIsZduyyP8oYuthxqsTqKilNaFKLAwKCft8ubWDXXq1NbxLhLFuXidtp5/F0dcFbW0KIQYfLyCxCRD1xSi06jo8/HXWv++UP3aZGILSkuB5ctyuW57Ufo6tNzb6vwG9MRzSp8Dlcf4o61+z6w7I0DDWyrbuO2u36H0/HhOn7L6tJwxVNh8Mkz8nh802H+vLWGzywvsDqOijPaUogClY1dzMxIGrEgqNizND+DU2am8fu3DuqAswo7yz5lRMQuIu+JyHPB20UiskFEykTkMRFxWpUtkrR299PSPUCRDjDHDRHhhrOLKKvv5I0DjVbHUXHGyq+e3wT2DLv9M+BOY8xsoAW40ZJUEaaysQtAi0KcuXRRLt5UF797q9LqKCrOWFIURGQWcCnw2+BtAS4Angg+5EHgSiuyRZrKpi4y3U6mJSVYHUWFkcth53NnFfDqvgbK6vUczip8rGop/BL438CxKSE9QKsx5tjuFoeBmSOtKCI3icgmEdnU0NAQ8qBW6vMNUtPSo62EOPXp5fk4HTYeWK+tBRU+YS8KInIZUG+MmdDpPI0x9xljlhljlnm9sX1076HmbvxGu47iVVaKiysXz+DJzTW0dvdbHUfFCStaCmcDnxCRg8CjBLqN7gLSReTYLrKzgLifAKaysQuXw0buND2hTrz64tlF9AwM8uD6KqujqDgR9qJgjPmeMWaWMaYQuA54xRjzGQJndrsm+LDrgWfCnS2SGGM42NhNgceNzaYHqMWr+blpXLwgm/vfrKC9d8DqOCoORNKO77cCt4hIGYExhvstzmOpo+299AwMateR4psXzqG918cDbx20OoqKA5YWBWPMq8aYy4LXK4wxZxpjZhtj/tEY02dlNquV13dhEyjyaFGId6fMnMbFC7L57RsVtPVoa0GFViS1FFSQMYayhk5mZbhxJegJdeKG2Eac20pEePC7n6S910fBRZ//wPK8fJ0GQ00tnfsoAjV19dPWM8DS/HSro6hwMv4PzXs13HPbj3D4/M+z5vs/GvqyoPNeqammLYUIVN4QOFipxJticRIVSZYXeejz+dlUpedaUKGjRSEClTd0kTstkWSXNuTU+7ypLublpPJedSvtOragQkSLQoRxTMumoaNPWwlqRCtLPAjwZplOlKdCQ4tChEmacxYAJV7d60h9WGpiAqcXZHCgvpOa1h6r46gYpEUhwrjnrsCT4iTdrTOHq5GdXpBBisvB6/sbAD2wUU0tLQoRpLGzD9esBdp1pE4qwW7j7Nke6jv6SD71IqvjqBijRSGC/H3nUURszNaioEZRmp3KjGmJZJx/Aw0dcX2cp5piWhQiyHPbjzDQVE1WinYdqZMTES6cn40tIZGfPLfb6jgqhmhRiBD17b1sqGyma+8bBM45pNTJZSY7aXv7Mf6y7Qgv76mzOo6KEVoUIsRfd9RiDHTvecPqKCqKtL3zBHOzU/jBn3fS2ecbfQWlRqFFIUI8t72W0uxUBpqqrY6ioonfx+1XL+Joey//+vye0R+v1Ci0KESAI609bKpq4bJFuVZHUVFoaX4GN51TzCMbD/HSbu1GUpOjRSEC/HVHLQCXnTbD4iQqWt2yei7zc9O49cntujeSmhQtChHgue21LJyRpifUURPmcti567rFdPT5WPPkdowxVkdSUUqLgsWqm7vZWt3KZYu0laAmZ252Kms+No+X99bzxw2HrI6jopQWBYs9s7UGQMcT1JT4wspCVs318tPndrP7SLvVcVQU0qJgIWMMT22pYXlRJnmZbqvjqBhgswl3XHsaGe4EvvrwFjp6dYptNT5aFCy0tbqVisYurl46y+ooKoZkpbj41XVLqGrq4ntP7dDxBTUuWhQs9OSWw7gcNj5+ao7VUVSMWV7s4durS3luey0Pb9TxBTV2WhQs0ucb5C/bavnowhxSExOsjqNi0JfPLWHVXC//5y+72VnTZnUcFSW0KFhk3d562noGuGrpTKujqBhlswl3BscXvqbjC2qMtChY5MktNUxPdfGR2VlWR1ExzJPi4tefWkp1Sw9rdHxBjYEWBQs0dfaxbm89Vy6ZicOufwIVWmcWZfLt1XN5fnutHr+gRqWfSBZ4+r0afH6jXUcqbG5eVcJ5pV5+quMLahRaFMLMGMPDGw+xJD+deTlpVsdRcSJw/MJiPClOvvLQFtp1fEGdgBaFMHunopmKhi4+s7zA6igqFogNERnTxZPi4r27v05VYwcln7ptzOvl5et7NZ44rA4Qbx7aUEVaokOntVBTw/i5Y+2+ca3y4x98j4zzb+DS+zdwWl76qI+/ZXXpBMOpaBT2loKI5InIOhHZLSK7ROSbweWZIvKiiBwI/swId7ZQa+jo44VdR7nm9DwSE+xWx1Fxqn3j0xR63Lx+oIG69l6r46gIY0X3kQ/4tjFmAXAW8FURWQCsAV42xswBXg7ejimPb6pmYNDw6eX5VkdRcc2wemEObqeDv+6opW9g0OpAKoKEvSgYY2qNMVuC1zuAPcBM4ArgweDDHgSuDHe2UMjLLwj0zdrs/Ptjr9FbtZ052akn7L9VKhySEuxccmoOHX0+XtlXb3UcFUEsHVMQkUJgCbAByDbG1AbvOgpkn2Cdm4CbAPLzI/8b9+HqQ9yxdh+VjV08u+0Il39kMXNvuPqEj9f+WxUuudOSOKvIw9sVTRRltevecAqwcO8jEUkBngS+ZYz5wMTvJnDY5YiHXhpj7jPGLDPGLPN6vWFIOjW2HGoh2WWnxJtidRSlhiwrzCB3WiLr9jbQ3qO7qSqLioKIJBAoCA8ZY54KLq4Tkdzg/blAzLRpj7b3crilhyV5Gdht2kWkIodNhI8uDMzS+8Kuo/h1Goy4Z8XeRwLcD+wxxtwx7K5ngeuD168Hngl3tlDZXNWC02HjlJnaPFeRZ1pSAueXejnS1sumgy1Wx1EWs6KlcDbwOeACEdkavFwC3A5cLCIHgIuCt6OeIz2XsvpOFs2chsuhu6GqyFSak8rc7BQ2VDZxVHdTjWthH2g2xrwJnKgP5cJwZgmHtDOvwi7C4jEcJKSUVUSEC0qnc6S1lxd2HuVTZ+bjdOiEB/FI/+oh1NDRR8qpFzI/N5Vklx48riKbK8HORxdm09ozwBsHGqyOoyyiRSGEfvtGBdgdLC2IuYOzVYyaleHm9IIMdh5pp7yh0+o4ygJaFEKkrr2XB9YfpGvXq2S4nVbHUWrMVhR78Ka6eHlPPV19PqvjqDDTohAiv37lAIN+Q9ubD1kdRalxsduEjy3MoX/Qz4t76qyOo8JMi0IIlDd08ujGaq47Mw9fm/5TqeiTmezknNlZVDV1k7LkUqvjqDDSohAC//e53SQl2PnWRXOtjqLUhC2aNY0Cj5uM82/gQF2H1XFUmGhRmGKv7qtn3b4Gvn7hbLJSXFbHUWrCRISL52djBnr55qNb6ff5rY6kwkCLwhTq6R/kh8/spDgrmS+sLLI6jlKTluxy0PS3u9hd285/vji+k/mo6KRFYQrd+dJ+qpt7+LerTtUDf1TM6CnbyKfOzOd/XqvgZR14jnn6yTVFNlc189s3KrjujDzOKvZYHUepKXXb5QtYOCONbz22lYONXVbHUSGkRWEKdPQO8M1HtzIzI4nvXzrf6jhKTbnEBDv3fvZ07Dbh5j9uprtfj1+IVVoUJskYw5qndlDb1ssvP7mE1MQEqyMpFRJ5mW5+dd0S9tV1sObJHRidZjsmaVGYpHtfq+D57bV896OlnK7TWagYt2qul++sLuXZbUf4+Qs68ByLdJa2Sfjbjlp+/sJeLluUyz+vKrY6jlJh8ZXzSjjS2sM9r5aT4U7gplUlVkdSU0iLwgS9Xd7ENx/dypK8dH5xzWkEzh2kVOwTEX5yxSm09gzwb3/dS3qSk2vPyLM6lpoiWhQm4O3yJm544F3yPW7uv/4Mkpx68hwVX+w24c5rF9PeM8Cap7ZjMHzyjHyrY6kpoGMK4/T3nUf54gMbmZWRxCNfOouMZJ0BVcUnp8PG/3zudD4yx8utT+7gVy8f0MHnGKBFYYyMMdy9royb/7iZeTlpPHLTWXhTdRoLFd/cTge//fwy/mHJTO54cT9ffXgL7b0DVsdSk6DdR2PQ3e/jX57awZ+3HuHKxTO4/epFJCZol5FSEGgx3HHtaczPTeVnf9/Htuo3+MU/LmJlSZbV0dQExG1LIS+/ABEZ9eKaUcqcr/2OP289gn/rM9z5ycVaEJQ6johw06oS/nTzChx24dO/2cA3HnlPj36OQnHbUjhcfYg71p54P+tBv+Hdg81sPNhMstPB6gXZ3Pmz3yByXxhTKhVdluZn8MK3VvHfr5Zz72vlPLf9CJecmstXzpvNghlpVsdTYxC3ReFkqpu7eXVfA83d/czLSeW8uV5c2jpQakwSE+zceePFHGluJ23ZFTzbeynPba+l78heuna9StfeN/B3t31ovVl5+VQfqrIgsRpOi8Iwnb0+3ihrYH9dJ2mJDi4/LZfirBSrYykVdYa3xHsHBtl9pJ29KafRMGMentU3M2NaEoUeN4VZyXiSnYgIt6wutTi1Ai0KAPgG/Ww73MaGyib8BpYXZbKsIAOH/bghF7HpQWoq/kzyfZ+YYGdpQQZLCzJo6uxjf10nlU1dvFXexFvlTaS4HBR63CTNXk5Xn49kl34sWSmuX32/MeypbeedimY6+3wUetycO9dLuvsExx4Y/0nHIUai335U1JvC970nxcWKFBcrSjx09vmoauqisrGL/XWdTL/6hyz+yVqWFWRybqmXVXO8zM9N1S9iYRaXRcEYQ9Ls5Ty04RDNXf1kp7lYvSCbvEy31dGUihspLgcLZ0xj4YxpDPoNP/jnT5JUuITXak/n7Yoibv/bXnwdTfQefI+eyi30Vr6Hv/f9c0XrGERoxGVRuOvlA0y/+of4jeGSU3KYPT1Fv40oZSG7Teir2sa//+ZxADr7fBxq6qaqKYVDGV56T70IgOw0FwWeZAo9bn5x7VIrI8esuCwKVy2ZxQ+/8w2+9rO7sNu0GCgVaVJcDhbMSGPBjDT8xlDf3kdVUxdVzd28W9nMxspmZn3jYb760BZWzc1i1VwvudOSrI4dE+KyKOR73HRue0ELglJRwCZCzrREcqYlsrzYQ+/AINXN3Tz+8B/YNN3D8ztqASjwuJmfk8a83FTm5aQxLyeVmRlJJBy/w4g6qbgsCkqp6JWYYGdOdipNf/sVDc//kv11nby2v56t1a3sre3ghd1HOTYvn00gJy2RGelJzMxI4umHfk9rbRWDXS3DLq2YvhMfeR1vYxcRVRRE5GPAXYAd+K0x5naLIymlIpiIUJqTSmlO6tCynv5B9td1sO9oB4dbujnc2kNNSw+bq1qQeReSufDDH3t2m+B22kl2OnA77bhd719/7N+/xeaqFqanuvCmumJ+mpuIKQoiYgfuBi4GDgPvisizxpjd1iZTSkWTJKed0/LSOS0v/UP3idj41+d30d3no6t/kO5+H939g3T3DdIVvN7WO0BtWy89A4MATL/q+1x9z/qh35HqcuBNdZEVLBLelMDPdHcCiQ47SU47iQk2Eh12EMCA34DBBH4aQ7/PT8/AID39g/QMDHLbT/+N9q4eJMGFzeFCEoIXhwuxOxCbA+z2wE+bHbE7cOx7iUNvPj3lr1/EFAXgTKDMGFMBICKPAlcAWhSUUlPEkJRgJynBjmeUR/r9hp6BQX5y4+W88PrbNHT0vX/p7KOxo489te283tFHR69vUqlsi68kHXDYhAS7DYddSLAFftpEsNkCYyt2EUQCLZv16343qec8EYmUk2KIyDXAx4wx/xS8/TlguTHma8c97ibgpuDNUuDYUTVZQGOY4kYi3X7dft3++Dae16DAGOMd6Y5IaimMiTHmPuBDU5WKyCZjzDILIkUE3X7dft3++N1+mLrXIJL21aoBhp/9e1ZwmVJKqTCJpKLwLjBHRIpExAlcBzxrcSallIorEdN9ZIzxicjXgBcI7JL6O2PMrnH8ing/+41uf3zT7VdT8hpEzECzUkop60VS95FSSimLaVFQSik1JOKLgoh8TET2iUiZiKwZ4X6XiDwWvH+DiBQGlxeKSI+IbA1e7g17+Ckwhu1fJSJbRMQXPNZj+H3Xi8iB4OX68KWeWpN8DQaHvQeicseFMWz/LSKyW0S2i8jLIlIw7L6ofw9Mcvvj4e9/s4jsCG7jmyKyYNh93wuut09EPjqmJzTGROyFwIBzOVAMOIFtwILjHvMV4N7g9euAx4LXC4GdVm9DGLa/EFgE/AG4ZtjyTKAi+DMjeD3D6m0K52sQvK/T6m0Iw/afD7iD17887H8g6t8Dk9n+OPr7pw27/gng78HrC4KPdwFFwd9jH+05I72lMDT1hTGmHzg29cVwVwAPBq8/AVwosXPGnFG33xhz0BizHfAft+5HgReNMc3GmBbgReBj4Qg9xSbzGsSCsWz/OmNMd/DmOwSO8YHYeA9MZvtjwVi2v33YzWTg2N5DVwCPGmP6jDGVQFnw951UpBeFmUD1sNuHg8tGfIwxxge0wdC0JkUi8p6IvCYi54Q6bAiMZftDsW4kmex2JIrIJhF5R0SunNJk4THe7b8R+NsE141Ek9l+iJO/v4h8VUTKgZ8D3xjPuseLmOMUQqAWyDfGNInI6cCfRWThcVVVxb4CY0yNiBQDr4jIDmNMudWhQkFEPgssA861OosVTrD9cfH3N8bcDdwtIp8GfgBMePwo0lsKY5n6YugxIuIApgFNwSZTE4AxZjOB/rS5IU88tSYz9UesTBsyqe0wxtQEf1YArwJLpjJcGIxp+0XkIuD7wCeMMX3jWTfCTWb74+bvP8yjwJUTXDfA6oGUUQZZHAQGx4p4f5Bl4XGP+SofHGh+PHjdS3BQhcAgTQ2QafU2TfX2D3vsA3x4oLmSwABjRvB6VG3/FLwGGYAreD0LOMBxg3SRfhnj/8ASAl965hy3POrfA5Pc/nj5+88Zdv1yYFPw+kI+ONBcwRgGmi3f6DG8KJcA+4N/9O8Hl/2EwDcCgETgTwQGUTYCxcHlVwO7gK3AFuByq7clRNt/BoG+wi6gCdg1bN0bgq9LGfBFq7cl3K8BsBLYEfzH2AHcaPW2hGj7XwLqgu/1rcCzsfQemOj2x9Hf/65hn3XrGFY0CLSeygmcYuDjY3k+neZCKaXUkEgfU1BKKRVGWhSUUkoN0aKglFJqiBYFpZRSQ7QoKKWUGqJFQSml1BAtCkoppYb8f27g1nHZDqUwAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n=100\n",
+    "p=14/50\n",
+    "\n",
+    "n_draws = 100_000\n",
+    "prior = pd.Series(np.random.uniform(0,1,size=n_draws))\n",
+    "\n",
+    "def generative_model(proba_visita):\n",
+    "    visita = np.random.binomial(100, proba_visita)\n",
+    "    return visita\n",
+    "\n",
+    "visita = list()\n",
+    "for j in prior:\n",
+    "    visita.append(generative_model(j))\n",
+    "\n",
+    "\n",
+    "posteriori = prior[list(map(lambda x: x==14, visita))]\n",
+    "sns.histplot(posteriori, kde=True)\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -171,10 +440,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    1011.000000\n",
+       "mean        0.145604\n",
+       "std         0.033704\n",
+       "min         0.059375\n",
+       "25%         0.122435\n",
+       "50%         0.143171\n",
+       "75%         0.166544\n",
+       "max         0.266472\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "posteriori.describe()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -185,10 +475,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0928636389966882,0.2048836265413594)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'({posteriori.quantile(0.05)},{posteriori.quantile(0.95)})')"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -197,6 +497,26 @@
     "What is the Maximum Likelihood Estimate?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1456035112806974"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "posteriori.mean()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -207,7 +527,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -221,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,