Syllabus
Bayes theorem and its use, prior and posterior distribution, methods to choose a prior distribution.
Statistical decision functions.
Bayes point estimators and their properties. Credible sets.
Bayes hypothesis testing, some special tests.
Some special bayesian approches, basics of MCMC.
Annotation
Prior and posterior distributions, conjugate families, Bayesian test and estimators, applications.