Syllabus for Bayesian Statistics

Bayesiansk statistik


  • 10 credits
  • Course code: 1MS900
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Mathematics A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-10
  • Established by:
  • Revised: 2021-10-15
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • Entry requirements:

    120 credits including 90 credits in mathematics. Participation in Regression Analysis. Participation in Inference Theory II. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Mathematics

Learning outcomes

On completion of the course, the student should be able to:

  • give an account of the philosophy of Bayesian models and their specific model assumptions;
  • choose suitable informative and non-informative prior distributions;
  • derive posterior distributions;
  • apply computer intensive methods for approximating the posterior distribution using R;
  • make correct inference from theoretical and approximated posterior distributions;
  • be able to interpret the results obtained by Bayesian methods.


Decision theoretic foundations. The minimaxity. The choice of prior distributions. Conjugate families. Bayesian point estimation. Bayesian tests. MCMC. Gibbs sampler. Bayesian model choice. Empirical Bayes extension.


Lectures and computer sessions.


Written examination at the end of the course. Compulsory assignments during the course.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Autumn 2022

Some titles may be available electronically through the University library.

  • Robert, Christian P. The Bayesian choice : from decision-theoretic foundations to computational implementation

    2. ed.: New York: Springer, cop. 2007

    Find in the library


Last modified: 2022-04-26