Learning outcomes

The course will introduce students to the basic ideas of Bayesian statistics using simple statistical models and will present some computational methods needed, such as the simulation of Markov chains. Regression analysis, hierarchical models and generalized linear models will also be introduced from the Bayesian viewpoint.

Contents

See objectives.

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. Robert, C. P., Casella, G., Monte Carlo Statistical Methods, 2nd ed. Springer 2005.
2. O’Hagan, A., Forster, J., Bayesian Inference, 2nd ed., volume 2B of "Kendall’s Advanced Theory of Statistics". Arnold 2004.
3. Gelman, A. et al, Bayesian Data Analysis, 2nd ed. Chapman & Hall / CRC 2004.

Belongs to following study modules