Bayesian Analysis

The aim of the course is to introduce the Bayesian approach to statistical inference. The course will develop the relevant methodology, theory and computational techniques necessary to its implementation. In the course single and multi-parameter models as well as the fundamental of Bayesian regression analysis will be discussed. Monte Carlo summaries of posterior distributions will be shown through the use of Gibbs sampler and Metropolis Hastings techniques.

At the end of the course, the students will be able to deal with the Bayesian inference. In particular, the students will understand how the Bayesian inference works for single and multiple parameters. As a further step, the students will improve their computational and methodologial skills through the use of R software for Bayesian modeling.