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Basic definitions and concepts of Bayesian statistics. Justification for the Bayes approach.
Poisson model. Computing posterior distributions. Noninformative and improper prior distributions.
Jeffreys' and conjugate prior distributions.
Conjugate priors in standard statistical models. Posterior mean in the normal model.
Statistical decision theory and Bayesian estimation. Risk functions. Bayesian and posterior risk.
Bayesian estimation for various loss functions (weighted quadratic loss). Bayesian confidence (credible) sets.
Testing statistical hypotheses. Bayes factor. A case study.
Bayes factor and statistical testing. Multidimensional normal model - estimation.
Normal model with unknown parameters. Multidimensional normal models with unknown covariance matrix.
Linear econometric models - linear regression. Autoregression processes.
Bayesian prediction - predictive distribution. Bayesian model choice.
Bayesian empirical approach. Hierarchical models. Case studies.
Problems and exercises
Problems and exercises
Complementary topics.
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