**[5 demi jours] - [Anglais]**

**Do you already heard about Bayesian statistics and want to know how to use it in practice?**

Bayesian statistics is an alternative approach to the frequentist inference (generally taught in basic statistical courses). More and more used by the scientist community, Bayesian statistics enables, among others, to include prior knowledge (through the prior distribution) in the model specification.

Objectifs de la formation

At the end of this course, the attendance will know the fundamental notions (prior distribution, posterior distribution, Bayes Factor) of Bayesian statistics. These notions are illustrated on classical statistical analyses. We will also highlight that JAGS (via R) and JASP are complemantory.

Prérequis

This training requires a basic knowledge of statistics (including notions of estimation). No prior knowledge are required for this training. Hereafter, two introductory links for those who never used R: Introduction to R programming ; Introduction to R objects

Contenu

- Comparison between the frequentist and Bayesian approaches
- Illustrations on classical statistical analyses : inference on a proportion, on one or on two means in normal samples, linear regression and logistic regression
- Bayesian estimation in JAGS (via R) and in JASP
- Convergence of a MCMC algorithm
- Answer to questions of the attendance.

Outils utilisés durant la formation

R / JASP

Méthodes et familles de méthodes abordées

Bayesian statistics

Student's t-test

ANOVA

Simple linear regression

Logistic regression

Multiple linear regression

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