Bayesian Inference, 5 credits
Course coordinator
Time
2024, week 7-15. Find schedule below.
Goals
The goal of this lecture course is to teach the fundamentals of Bayesian statis- tical inference. It covers basic theory, supplemented with methodological and computational aspects.
Litterature
The course is based on the textbook: S. Zwanzig and R. Ahmad: Bayesian Inference , CRC Press 2024 (in production). The registered students can download lecture notes related to the book.
Lectures
The course consists of 14 lectures + project presentation. Note that, the following list of content is preliminary. It is possible to adapt the course to research projects of the PhD students.
- Bayesian modelling (1 lecture, week 7), key words: Bayesian inference principle, advantages
- Choice of prior (3 lectures, week 7), key words: subjective and objective priors, conjugate, non{informative, reference priors
- Decision theory (3 lectures, week 8), key words: Bayes decision rules, minimax criterion, worst case prior
- Asymptotic theory (2 lectures, week 10), key words: Consistency, Schwarz Theorem
- Linear model (1 lecture, week 10) key words: explicite formularies for posteriors
- Bayes methods (2 lectures, week 11), key words: regularized estimators, HPD regions, Bayes Factors
- Computer intensive methods (2 lectures, week 11), key words: Gibbs sampling, MCMC, ABC
- Project presentations (week 15)
Examination
2 Obligatory home works
Team work is recommended
- Assignment 1 (Bayes Modelling and Decision Theory) published published 21/2, deadline week 1/3
- Assignment 2 (Bayes Methods) published 1/3, deadline week 12/3
Mini-Project
Application of a Bayes model to an own chosen data set. Proposal (max one page) deadline week 11, 12/3, with feedback one day later, presentation (20 min) in week 12, 20/3 10.15-12.00, written summary (max 3 pages) deadline week 15. Team work is recommended.
Oral examination or alternatively Personal home exam
Week 15. The students have the choice.
Organization
We will use the webside "studium". PH-D students has to be registered man-
ually in studium. Please send a email to me: zwanzig@math.uu.se The exact
schedule will be published next week, examination times by personal agreement
but no later than two weeks after presentation.
Schedule
Week 7
Day | Date | Time | Place |
---|---|---|---|
Mon | 12/2 | 13.15-15.00 | ˚Ang 1167 |
Tue | 13/2 | 10.15-12.00 | ˚Ang 4005 |
Wed | 14/2 | 13.15-15.00 | ˚Ang 1167 |
Week 8
Day | Date | Time | Place |
---|---|---|---|
Tue | 20/2 | 13.15-15.00 | ˚Ang 2004 |
Wed | 21/2 | 13.15-15.00 | ˚Ang 1167 |
Thu | 22/2 | 13.15-15.00 | ˚Ang 2004 |
Fri | 23/2 | 13.15-15.00 | ˚Ang 2004 |
Week 9
Day | Date | Time | Place |
---|---|---|---|
Wed | 28/2 | 13.15-15.00 | ˚Ang 2004 |
Thu | 29/2 | 13.15-15.00 | ˚Ang 1167 |
Fri | 1/3 | 13.15-15.00 | ˚Ang 1167 |
Week 11
Day | Date | Time | Place |
---|---|---|---|
Tue | 12/3 | 10.15-12.00 | ˚Ang 1167 |
Wed | 13/3 | 13.15-15.00 | ˚Ang 1167 |
Thu | 14/3 | 13.15-15.00 | ˚Ang 4003 |
Fri | 15/3 | 10.15-12.00 | ˚Ang 4003 |
Week 12
Wed | 12/3 | 10.15-12.00 | ˚Ang 1167 |
Welcome!