Bayesian Statistics

Course content

  • The Bayesian paradigm
  • Sufficiency and likelihood
  • Prior and posterior distributions
  • Decision theoretic foundations
  • Bayesian parameter estimation
  • Tests and confidence regions
  • Bayesian calculations and Monte Carlo methods
  • Bayes factors and model choice
  • Empirical Bayes methods

MSc Programme in Statistics

MSc Programme in Mathematics-Economics 

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 software for modelling and Bayesian computation
  • Ability to perform Bayesian analysis of statistical models


Lectures and theoretical exercises

Basic understanding of mathematical statistics including conditional distributions. Stat1 + Stat2 or equivalent is sufficient.

Identical to NMAK16002U Bayesian Statistics.

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
  • Exercises
  • 21
  • Exam
  • 27
  • Preparation
  • 130
  • English
  • 206