diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 95bfcb9..9b1a0a7 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,13 +11,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "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 +32,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.6" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PA = 1/2\n", + "PB = (30+20)/80\n", + "PBdA = 30/40\n", + "PAdB = (PBdA*PA)/PB\n", + "PAdB" + ] }, { "cell_type": "markdown", @@ -45,10 +63,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.4" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PA = 1/2\n", + "PB = (30+20)/80\n", + "PBdA = 20/40\n", + "PAdB = (PBdA*PA)/PB\n", + "PAdB" + ] }, { "cell_type": "markdown", @@ -59,10 +94,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that a chocolate cookie is drawn from Bowl 1: 0.3333333333333333\n" + ] + } + ], + "source": [ + "choco_bowl_1 = (10/40)*(1/2)/((10/40)*(1/2) + (20/40)*(1/2))\n", + "print(f'Probability that a chocolate cookie is drawn from Bowl 1: {choco_bowl_1}')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that a chocolate cookie is drawn from Bowl 2: 0.6666666666666666\n" + ] + } + ], + "source": [ + "choco_bowl_2 = (20/40)*(1/2)/((10/40)*(1/2) + (20/40)*(1/2))\n", + "print(f'Probability that a chocolate cookie is drawn from Bowl 2: {choco_bowl_2}')" + ] }, { "cell_type": "markdown", @@ -95,10 +161,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that a yellow candy is drawn from Bag 1: 0.5882352941176471\n" + ] + } + ], + "source": [ + "yellow_bag_1 = (0.2)*(1/2)/((0.2)*(1/2) + (0.14)*(1/2))\n", + "print(f'Probability that a yellow candy is drawn from Bag 1: {yellow_bag_1}')" + ] }, { "cell_type": "markdown", @@ -109,10 +186,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that a yellow candy is drawn from Bag 2: 0.411764705882353\n" + ] + } + ], + "source": [ + "yellow_bag_2 = (0.14)*(1/2)/((0.2)*(1/2) + (0.14)*(1/2))\n", + "print(f'Probability that a yellow candy is drawn from Bag 2: {yellow_bag_2}')" + ] }, { "cell_type": "markdown", @@ -123,10 +211,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that a green candy is drawn from Bag 1: 0.3333333333333333\n" + ] + } + ], + "source": [ + "green_bag_1 = (0.1)*(1/2)/((0.1)*(1/2) + (0.2)*(1/2))\n", + "print(f'Probability that a green candy is drawn from Bag 1: {green_bag_1}')" + ] }, { "cell_type": "markdown", @@ -141,10 +240,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La probabilidad de que este detras de la puerta A es 0.3333333333333333\n" + ] + } + ], + "source": [ + "prob_a = (1/2) * (1/3) / ((1/3)*(1/2) + (1/3)*(1))\n", + "print(f'La probabilidad de que este detras de la puerta A es {prob_a}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La probabilidad de que este detras de la puerta C es 0.6666666666666666\n" + ] + } + ], + "source": [ + "prob_c = (1/2) * (2/3) / ((1/3)*(1/2) + (1/3)*(1))\n", + "print(f'La probabilidad de que este detras de la puerta C es {prob_c}')" + ] }, { "cell_type": "markdown", @@ -157,10 +285,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def generative_model(proba_subs):\n", + " subs = np.random.binomial(100, proba_subs)\n", + " return subs\n", + "\n", + "\n", + "n_sends = 100_000\n", + "prior = pd.Series(np.random.uniform(0, 1, size=n_sends))\n", + "\n", + "subs_page = []\n", + "for i in prior:\n", + " subs_page.append(generative_model(i))" + ] }, { "cell_type": "markdown", @@ -171,10 +311,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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