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DE-MX-PT-Alejandro Pedrero Montiel #22

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222 changes: 197 additions & 25 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,13 +11,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
Expand All @@ -31,10 +32,39 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": []
"source": [
"import numpy as np\n",
"\n",
"def bayes_rule(priors, likelihoods):\n",
" marg = sum(np.multiply(priors, likelihoods))\n",
" post = np.divide(np.multiply(priors, likelihoods), marg)\n",
" return post"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.6, 0.4])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"priors = [1/2, 1/2]\n",
"likelihoods = [30/40, 20/40]\n",
"bayes_rule(priors, likelihoods)"
]
},
{
"cell_type": "markdown",
Expand All @@ -45,10 +75,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": []
"source": [
"#Para el BOWL 1 = 0.6\n",
"#Para el BOWL 2 = 0.4"
]
},
{
"cell_type": "markdown",
Expand All @@ -59,10 +92,25 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"array([0.33333333, 0.66666667])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chocoprior = [1/2, 1/2]\n",
"chocolike = [10/40, 20/40]\n",
"bayes_rule(chocoprior, chocolike)"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -95,10 +143,47 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"array([0.58823529, 0.41176471])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bags = [1/2, 1/2]\n",
"yellow_candy = [0.20,0.14]\n",
"bayes_rule(bags, yellow_candy)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.33333333, 0.66666667])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bags = [1/2, 1/2]\n",
"green_candy = [0.10,0.20]\n",
"bayes_rule(bags,green_candy)"
]
},
{
"cell_type": "markdown",
Expand All @@ -112,7 +197,9 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# Probability of 67%"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -141,10 +228,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"array([0.33333333, 0.66666667, 0. ])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#La probabilidad de elegir la puerta correcta es 1/3. \n",
"#0.33 por cada \n",
"doors = [1/3, 1/3, 1/3]\n",
"chance = [1/2,1,0]\n",
"bayes_rule(doors, chance)\n"
]
},
{
"cell_type": "markdown",
Expand All @@ -157,10 +261,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": []
"source": [
"new_users = 14\n",
"def modelo_generativo(parametro):\n",
" resultado = np.random.binomial(100, parametro)\n",
" return resultado"
]
},
{
"cell_type": "markdown",
Expand All @@ -171,10 +280,43 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1e451c19eb8>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"datos = list()\n",
"n_draws = 100000\n",
"prior = pd.Series(np.random.uniform(0, 1, size=n_draws))\n",
"\n",
"for p in prior:\n",
" datos.append(modelo_generativo(p))\n",
" \n",
"posterior = prior[list(map(lambda x: x == new_users, datos))]\n",
"posterior.hist()"
]
},
{
"cell_type": "markdown",
Expand All @@ -185,10 +327,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.08928295277336795 | 0.19081587446568182\n"
]
}
],
"source": [
"print(posterior.quantile(.025),'|',posterior.quantile(.90))"
]
},
{
"cell_type": "markdown",
Expand All @@ -197,6 +349,26 @@
"What is the Maximum Likelihood Estimate?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum Likelihood Estimate: 0.14 | 0.1342828077314344\n"
]
}
],
"source": [
"rounded = posterior.round(2)\n",
"mode = rounded.mode()[0]\n",
"probability = list(rounded).count(mode)/len(rounded)\n",
"print('Maximum Likelihood Estimate: ', mode, '|',probability)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -221,7 +393,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.7.3"
}
},
"nbformat": 4,
Expand Down