General description

A compulsory advanced studies course for main subject students.

Learning outcomes

The student is exposed to the basic ideas of Bayesian statistics, nonparametric inference and computationally intensive methods. Furthermore, he gets an idea of Markov chains and their applications.

Contents

The basic concepts of Bayesian statistics include prior, posterior and posterior predictive distributions. Computationally intensive methods comprehend permutation tests, jackknife and bootstrap methods, cross-validation, and MCMC simulation. Also Markov chains and statistical model selection are dealt with.

Teaching language

Finnish

Evaluation

Numeric 1-5.

Recommended year of study

1. year autumn
1. year spring

Study materials

1. Casella, G., Berger, R. L., Statistical inference. Brooks/Cole 2002.
2. Davison, A., Statistical models. Cambridge University Press 2003.
3. Garthwaite, P. H., Jolliffe, I. T., Jones, B., Statistical inference. Prentice Hall 2002.
4. Rohatgi, V. K., Statistical inference. Wiley 2003.
5. Ross, S. M., Introduction to probability models. Academic Press 2002.
6. Williams, D., Weighing the odds, a course in probability and statistics. Cambridge University Press 2001.
   

Belongs to following study modules