Basic concepts and practical applications of Bayesian statistics with R and JASP

[5 half days] - [English]

Have you heard of Bayesian statistics and want to give them a try?

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.

Training aims
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.

Prerequisites
This training requires a basic knowledge of statistics (including notions of estimation). Basic skills in R are mandatory. You have no basic skills in R and still want to attend this training, please contact us (smcs-stat@uclouvain.be).

Content

• 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.

Rate
This training is eligible for the training voucher scheme of the Walloon Region.
If you wish to use training vouchers as part of your registration, please visit the following page for further information and contact our administration office.

Tools used during training
JASP

Methods and method families discussed
Bayesian statistics
   Student's t-test
   ANOVA
   Simple linear regression
   Logistic regression
   Multiple linear regression