Description: During the last decades Bayesian statistics has gained enormous popularity as an elegant and powerful computational tool to perform statistical analysis in complex stochastic models as applied in engineering, science and medicine. Bayesian statistics offers an alternative approach to traditional data analysis by including prior knowledge about the model parameters in form of a prior distribution. Using Bayes formula the prior distribution is updated o the posterior distribution by incorporating the observed data by means of the likelihood. Subsequently statistical inference about the unknown model parameters is derived from the posterior distribution. However the posterior distribution is often intractable due to high-dimensional complex integrals implying that approximate stochastic simulation techniques such as Markov Chain Monte Carlo (MCMC) methods become crucial. This course reviews the basics ideas behind Bayesian statistics and Markov chain Monte Carlo (MCMC) methods. Background on Markov chains will be provided and subjects such as Metropolis and Metropolis-Hastings algorithms, Gibbs sampling and output analysis will be discussed. Furthermore, graphical models will be introduced as a convenient tool to model complex dependency structures within a stochastic model. The theory will be demonstrated through different examples of
applications and exercises, partly based on the software packages R, BUGS and JAGS.

Prerequisites: The course is accessible to those new to these subjects; however, a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is expected.

Organizer: Jesper Møller,

Lecturers: Professor Jesper Møller and Head of Department Søren Højsgaard, email:


Time: 15-17 and 22-24 June, 2015 9-16

Place: Aalborg University, Fredrik Bajers Vej 7E/3-109
Fredrik Bajers Vej 7E/3-209

Zip code: 9220

City: Aalborg

Number of seats: 40

Deadline: 26 May

Material: Details and material for the course can be found here: