Learning outcomes

After the course, the student will be able to apply Markov chains in statistical modelling and program the estimation of some simple models using Markov chain Monte Carlo methods. He will also be able to do model critique and comparison with Bayesian methods. Further, he will be able to perform the Bayesian analysis of some of the most commonly used statistical models.

Contents

Markov chains, MCMC methods, model checking and comparison, commonly used statistical models, such as hierarchical and regression models, binomial and count data models.

Evaluation

Belongs to following study modules

2018–2019

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

MTTTS13 Introduction to Bayesian Analysis 2