Learning outcomes

The goal is to get acquainted with the most common methods of computational statistics and to learn how to implement them.

Contents

Random variable generation, permutation tests, bootstrap and jackknife, cross validation, kernel density estimation, local regression, Gibbs sampling, Metropolis-Hastings algorithm, importance sampling, slice sampling.

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. Davison, A. C., Hinkley, D. V., Bootstrap methods and their application. Cambridge University Press 1997.
2. Rizzo, M. L., Statistical computing with R. Chapman & Hall/CRC 2007.
3. Robert, C. P., Casella, G., Introducing Monte Carlo methods with R. Springer Verlag 2009.

Belongs to following study modules