{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Illustration du théorème de la limite centrale" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "N = 10000 # nombre de tirages pour le calcul de la moyenne empirique\n", "M = 5000 # nombre de repetitions\n", "\n", "X = np.random.exponential(0.5,(N,M))\n", "# X = np.random.rand(N,M)<0.5 # Bernoulli 1/2, convergence plus lente\n", "\n", "esp = 0.5\n", "var = 0.25\n", "\n", "moyenneEmp_N = np.sum(X,axis=0)*(1./N) # calcule pour chaque tirage m la moyenne empirique\n", "erreurNormalisee_N = ??? # recentrer et normaliser l'erreur empirique pour avoir une convergence vers N(0,1)\n", "\n", "# Affichage\n", "plt.hist(erreurNormalisee_N, density=\"True\", bins=int(np.sqrt(M)), label=\"erreur normalisee\")\n", "\n", "x = np.linspace(-5,5,100)\n", "\n", "densiteGaussienne = np.exp(-x**2/2)/np.sqrt(2*np.pi)\n", "plt.plot(x, densiteGaussienne, color=\"red\", label=\"densite gaussienne\")\n", "plt.legend(loc=\"best\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.10.6" }, "name": "Uncertain Mortality.ipynb" }, "nbformat": 4, "nbformat_minor": 4 }