General description

A compulsory advanced studies course for main subject students.

Learning outcomes

The course will familiarise students with the basics of Bayesian statistics, such as the concepts of prior and posterior distributions, the choice of Bayes estimators and Bayesian credible intervals. The course will also introduce computationally intensive methods, including permutation tests, jackknife and bootstrap methods, cross-validation, importance sampling and Markov Chain Monte Carlo methods. Other topics covered will include Markov chains and statistical model selection.

Contents

See objectives.

Teaching language

Finnish

Evaluation

Numeric 1-5.

Recommended year of study

1. year autumn
1. year spring

Study materials

1. Garthwaite, P. H., Jolliffe, I. T., Jones, B., Statistical Inference. Prentice Hall 2002;.
2. Ross, S. M., Introduction to Probability Models. Academic Press 2002.
3. Silvey, S. D., Statistical Inference. Chapman&Hall 2000.
4. Williams, D., Weighing the Odds, a Course in Probability and Statistics. Cambridge University Press 2001.
5. Davison, A., Statistical Models. Cambridge University Press 2003.
6. Casella, G., Berger, R. L., Statistical inference. Brooks/Cole 2002.
7. Rohatgi, V. K., Statistical Inference. Wiley 2003.

Belongs to following study modules