{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3cbf95c3-c1c9-411b-8f6e-1be6e562f043",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "193e73dc-4506-4db1-b2fa-661791296277",
   "metadata": {},
   "source": [
    "# Konvoluce dvou nez. normálních veličin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f90a2d0f-2358-4ffa-9c9e-0fd286adca88",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = stats.norm.rvs(loc=2, size=10**6)\n",
    "y = stats.norm.rvs(loc=3, size=10**6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80d9a1cb-de7d-4b84-8693-83d705c27a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.hist(x, bins=100);\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.hist(x+y, bins=100);"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}