Bayesian Statistics

Course content


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  • 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

PhD programme in Statistics

Learning outcome


Basic knowledge of the topics covered


  • 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



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


Lectures and theoretical exercises

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

Basic understanding of mathematical statistics including conditional distributions

7,5 ECTS
Type of assessment
Written assignment, 27 hours
Written assignment
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Theory exercises
  • 18
  • Practical exercises
  • 3
  • Exam
  • 27
  • Preparation
  • 130
  • English
  • 206