Learning outcomes

The course introduces students to the basic ideas of Bayesian statistics using simple statistical models and presents useful computational methods.

Contents

Basic ideas of Bayesian statistics, normal & binomial models, count & lifetime models, likelihood principle, 2-parameter normal model, comparing 2 populations, hierarchical & regression models, MCMC, model comparison, decision theory.

Teaching language

Finnish

Evaluation

Numeric 1-5.

Recommended year of study

1. year autumn
1. year spring
2. year autumn
2. year spring

Study materials

1. Gelman, A., et al, Bayesian data analysis, 2nd ed. Chapman & Hall / CRC 2004.
2. O’Hagan, A., Forster, J., Bayesian inference, 2nd ed., volume 2B of "Kendall’s advanced theory of statistics". Arnold 2004.
3. Gilks, W.R., et al, Markov chain Monte Carlo in practice, Chapman & Hall, 1996.

Belongs to following study modules