• The Bayesian paradigm
• Sufficiency and likelihood
• Prior and posterior distributions
• Decision theoretic foundations
• Conjugate prior distributions
• Default prior distributions
• Bayesian parameter estimation
• Bayesian computation
• Bayes factors and model choice
• Bayesian asymptotics
• Empirical Bayes methods

Knowledge:

Basic knowledge of the topics covered

Skills:

• Discuss and understand basics of the Bayesian paradigm
• Understand how decision theory underpins Bayesian inference
• Understand methods for constructing prior distributions
• Discuss and understand basic principles for Bayesian model choice

Competences:

• Ability to use standard software for simple modelling and Bayesian computation
• Ability to construct and perform a Bayesian analysis of statistical models

C. P. Robert. The Bayesian Choice. 2nd edition. Springer-Verlag 2001. Paperback edition 2007.