732G43 · 7.5 credits · Basic level (G2F) · Statistik
Bayesian Statistics
Active · next start HT 2026
60%
passed the latest regular sitting
DAT2 · 16 Jan 2026 · 20 students
68%
passed the regular sittings, all sittings over the last 5 years
DAT2 · 37 students
Unusually hard in 2025/26
60% passed the ordinary exam in 2025/26 (20 students), against 78% on average in earlier years (17 students).
Part of 1 programme
Other module
DAT2 Examination
- VG
- G
- U
Main rounds
68% passed · 37 students
- 60%20
- 73%11
- 83%6
Later rounds
57% passed · 7 students
- 57%7
- Pass a later round: 57% (7 students), compared with 68% (37 students) on the ordinary exam
- Get VG: 25% (44 students)
Show small sittings (4)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 75%4
- 50%2
- 50%2
- 100%2
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
INL2 Examination
- G
Main rounds
100% passed · 36 students
- 100%9
- 100%10
- 100%11
- 100%6
Later rounds
No sittings with at least 5 students.
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 100%1
- 100%1
Hand-in or lab
INL1 Examination
- G
Main rounds
100% passed · 18 students
- 100%7
- 100%11
Later rounds
No sittings with at least 5 students.
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
Other module
DAT1 Examination
- G
- U
Main rounds
89% passed · 18 students
- 88%8
- 90%10
Later rounds
No sittings with at least 5 students.
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 0%2
- 100%1
About the course
Aim
After completion of the course, the student should be able to:
- explain the main concepts in Bayesian statistics
- explain the differences between frequentist and Bayesian statistics
- use the most common statistical methods within Bayesian inference
- choose relevant models for Bayesian inference of various practical problems
- apply Bayesian inference using statistical software
Content
The course consists of general concepts and methods in Bayesian statistics. In addition, MCMC is implemented as a tool to estimate more complicated models in which an analytical form of the posterior is not possible.
Contents of the course:
- subjective probabilities
- Bayes' theorem
- prior distribution
- sensitivity analysis of prior distributions
- ikelihood function
- posterior distribution
- credible interval
- model evaluation
- model selection
- MCMC
- Examiner
- Josef Wilzén
- Main field of study
- Statistik