Bayes theorem, properties of the posterior probability, I-divergence and the information contained in an experiment, non-informative priors (different approaches), conjugate priors, MCMC methods, statistical decision rules, Bayes estimators in general and in particular in linear models, Bayes conception of testing hypotheses and of interval estimation.

Type of methodology: Combination of lecture and hands-on

Participants receive the certificate of attendance: Yes

Paid training activity for participants: Yes, for all

Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge