{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to predict catastrophic events ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the end of that short but preciser tutorial, you'll understand what is a GEV (Generalized extreme value distribution) and how to fit it on your data in python. \n", "\n", "What we need to think about before speaking of extreme values statistics, is that in general statistics focus on average values and observable phenomenoms, for which we **do have datas**. Extreme values statistics is different from \"average\" statistics in this regard, indeed as we will see, we here try to model something for which **we have no data!** You will see soon what we mean by that. Before getting there I want to show you why extreme values modelling is an actual problem, of high interest for insurance companies and public services. Let's start with two examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###