732A91 · 6 credits · Advanced level (A1F) · Statistik
Bayesian Learning
Active · next start VT 2027
62%
passed the latest regular sitting
DAT2 · 4 Jun 2025 · 26 students
61%
passed the regular sittings, all sittings over the last 5 years
DAT2 · 112 students
Part of 1 programme
Other module
DAT2 Examination
- A
- B
- C
- D
- E
- F
Main rounds
61% passed · 112 students
- 62%26
- 29%24
- 80%35
- 63%27
Later rounds
53% passed · 109 students
- 54%13
- 66%29
- 42%12
- 20%15
- 44%9
- 60%15
- 78%9
- 57%7
- Pass a later round: 53% (109 students), compared with 61% (112 students) on the ordinary exam
- Get B or A: 10% (221 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%1
- 50%4
- 50%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Exam
TENT Examination
- B
- C
- D
- E
- F
Ordinary exams
75% passed · 16 students
- 75%16
Re-exams
No sittings with at least 5 students.
- Get B: 6% (16 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%2
- 33%3
Hand-in or lab
LAB2 Laboratory work
- P
Main rounds
100% passed · 100 students
- 100%24
- 100%25
- 100%31
- 100%20
Later rounds
100% passed · 29 students
- 100%24
- 100%5
- Pass a later round: 100% (29 students), compared with 100% (100 students) on the ordinary exam
Show small sittings (8)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%3
- 100%1
- 100%1
- 100%4
- 100%2
- 100%1
- 100%1
- 100%3
Hand-in or lab
LAB1 Laboratory work
- P
No sittings with at least 5 students.
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
About the course
Aim
After completion of the course the student should at an advanced level be able to:
- account for the main differences between Bayesian and frequentist inference
- analyze basic statistical models using a Bayesian approach and correctly interpret the results
- critically analyze and use Bayesian models for prediction and decision making
- implement more advanced statistical models, including those occurring in current research problems, using modern simulation methods
- perform Bayesian model inference on practical problems using programming language applicable for statistical analysis
Content
The course covers the following topics:
- Subjective probability
- Bayes theorem
- Likelihood
- Prior and posterior distribution
- Regularization priors
- Classification
- Marginalization
- Posterior approximation
- Bayesian analysis of the following models: Bernoulli, Normal, Poisson, Multinomial, Multivariate normal; Linear and nonlinear regression, Binary regression
- Prediction
- Decision theory
- Markov Chain Monte Carlo, including Gibbs sampling
- Mixture models
- Bayesian variable selection
- Model selection
- Examiner
- Bertil Wegmann
- Main field of study
- Statistik