Component
École Nationale Supérieure d'Électrotechnique d'Électronique d'Informatique d'Hydraulique et des Télécommunications
Semester
Printemps
Objectives
The aim of this course is to enable the future engineer to build a mathematical model based on the observation of a random phenomenon and a collection of experimental or sampling data. This construction goes from the choice of model to its precise adjustment and its validation. This model must then allow a better understanding or analysis of the phenomenon and lead, if necessary, to decision-making or forecasts.
Description
1st course: Normal distributions; mean and variance.
2nd course: Basis of estimation; unbiased estimation of minimum variance.
3rd course: Fisher Information; Cramer-Rao inequality; maximum likelihood.
4th course: Basis of hypothesis testing; Lemma of Neyman and Pearson.
5th course: Likelihood ratio test; Linear regression. 6th course: Multilinear regression.
7th course: Complements; revisions. Tutorials
TD 1: Mean-variance independence, Gaussian case.
TD 2: Estimate (1).
TD 3: Estimation (2).
TD 4: Hypothesis testing.