Course: Bayesian Statistics

Course type: compulsory
Lecturer: Dejan Velušček, Ph.D., Assistant Professor

Study programme and level Study field Academic year Semester
Applied statistics, second level Mathematical statistics 2nd 1st

For the timeline see Curriculum.


  • Enrolment into the relevant academic year.

Content (Syllabus outline):

  • Bayesian models with one and more parameters. Connection with standard statistical methods. Hierachical models. Testing of models and sensitivity analysis. Bayesian  design of experiment.
  • Bayesian approach to evidence synthesis of multiple surveys, power priors, analysis of dependence of synthesis analysis on previous surveys.
  • Introduction into regression analysis. Analysis of variance and covariance. Hypothesis testing via Bayes factor, complexity and fit. Posterior probabilities of hypotheses – models, and influence of priors on them, training sample.
  • More on posterior probabilities, estimating parameters, central credibility interval, the importance of conjugated distributions. Gibbs sampler, convergence of estimates, algorithm Metropolis-Hastings. Posterior simulations. Some other specific models of Bayesian anlysis

Objectives and competences:

Basic knowledge of Bayesian statistics is acquired.

Bayesian methods are of great importance in practice. Therefore, experts with practical knowledge will present their experience in class.

Intended learning outcomes:
Understanding of basic concepts of Bayesian statistics.



Main contact:
e-mail: info.stat (at)

Contact for administrative questions (enrolment, technical questions):
Katarina Erjavec Drešar
University of Ljubljana, Faculty of electrical engineering, Tržaška cesta 25, 1000 Ljubljana.
room num.: AN012C-ŠTU
phone: 01 4768 209
e-mail: katarina.erjavec-dresar (at)