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Archived Curricula Guide 2011–2012
Curricula Guide is archieved. Please refer to current Curricula Guides
TILTS1 Statistical Inference II 10 ECTS

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.


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


Modes of study


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

School of Information Sciences
Archived Teaching Schedule. Please refer to current Teaching Shedule.
School of Information Sciences