Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After the course, the student can explain the central concepts in Bayesian statistics, and name steps of the Bayesian modeling process. The student can recognize usages for common (i.e. those presented during the course) statistical models, and formulate the models in these situations. The student can compare the most popular Bayesian simulation methods, and implement them. The student can use analytic and simulation based methods for learning the parameters of a given model. The student can estimate the fit of a model to data and compare models.

Credits: 5

Schedule: 07.09.2020 - 03.12.2020

Teacher in charge (valid 01.08.2020-31.07.2022): Aki Vehtari

Teacher in charge (applies in this implementation): Aki Vehtari

Contact information for the course (valid 14.08.2020-21.12.2112):

Lecturer:, TAs: see the course web page

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English


  • Valid 01.08.2020-31.07.2022:

    Bayesian probability theory and bayesian inference. Bayesian models and their analysis. Computational methods, Markov-Chain Monte Carlo.

  • Applies in this implementation:

    Bayesian inference, Basic model building blocks, Hierarchical models, Computational methods, Markov chain Monte Carlo, inference diagnostics, Stan and probabilistic programming, Model checking, Evaluating and comparing models, Decision analysis, Large sample properties and Laplace approximation.

Assessment Methods and Criteria
  • Applies in this implementation:

    Exercises (67%) and a project work (33%). Minimum of 50% of points must be obtained from both the exercises and project work.

  • Valid 01.08.2020-31.07.2022:

    Lectures 10x2h, computer exercises 10x2h, independent studying (text book, programming, home exercise report), final exam


Study Material
  • Applies in this implementation:

    Bayesian Data Analysis, 3rd ed, by by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Chapters 1-7,9-12 and extra material shared on the course web site

Substitutes for Courses
  • Valid 01.08.2020-31.07.2022:

    Replaces courses BECS-E2601 Bayesian Data Analysis, Becs-114.2601 Bayesian Modelling and Becs-114.1311 Introduction to Bayesian Statistics.

  • Valid 01.08.2020-31.07.2022:

    Differential and integral calculus, basics of probability and statistics, basics of programming (R or Python). Recommended: matrix algebra.



Registration and further information