MVE187/MSA101 Computational methods for Bayesian statistics
Course PM
This page contains the program of the course: lectures, recommended exercises and computer labs. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.
Program
The schedule of the course is in TimeEdit.
Lectures (preliminary schedule)
NOTE: Some lecture times have been switched with exercise times to accommodate the schedules of other courses.
| Day | Sections | Content |
|---|---|---|
| Tuesday 3/9 13:15 - 15:00 | Course intro and Lecture 1. B2.1-B2.4 (meaning chapter 2 sections 1 through 4 from Bishop; see literature list). A2, A3, A4. | Course intro. Introduction to the Bayesian paradigm. Issues with classical frequentist inference. |
| Thursday 5/9 15:15 - 17:00 | Lecture 2. R code. B2.1-B2.4. A2, A3, A4. | Basic computations using conjugacy. Using discretization. The exponential family of distributions. |
|
Tuesday 10/9 13:15 - 15:00 |
Lecture 3. R code. B2.1-B2.4. A2, A3, A4. | Mixtures. Some multivariate conjugacies. Low dimensional Bayesian inference. |
| Thursday 12/9 13:15 - 15:00 | Lecture 4. R code. B11.1 (until B11.1.6); B11.4; A5; RC2, RC3. | Inference by simulation. Monte Carlo integration. Basic simulation methods. |
| Tuesday 17/9 13:15 - 15:00 | Lecture 5. R code. B 11.2; A6; RC6. | Introduction to Markov chain Monte Carlo (MCMC) methods. |
| Thursday 19/9 15:15 - 17:00 | Lecture 6. R code. B 11.2; A6; RC6. | MCMC. Random walk. Independent proposal. Convergence; checking convergence. Burn-in. Smart proposals. |
| Tuesday 24/9 13:15 - 15:00 | Lecture 7. R code. B11.2; A6; RC6. | MCMC. Hierarchical models. Gibbs sampling. |
| Thursday 26/9 13:15 - 15:00 | Lecture 8. R code. A7; RC7. | MCMC. Examples. Slice sampling. Missing data. More advanced alternatives. |
| Tuesday 1/10 13:15 - 15:00 | Lecture 9. R code. B1.6, 9.4 (9.2, 9.3); RC5.4. | Some information theory. The EM algorithm. |
| Thursday 3/10 13:15 - 15:00 | Lecture 10. B8. | Introduction to Graphical Models. |
| Tuesday 8/10 13:15 - 15:00 | Lecture 11. R code. B13.2 | Inference for Graphical Models. |
| Thursday 10/10 15:15 - 17:00 | Lecture 12. R code. B10.1 (B10.2) | Variational Bayes |
| Tuesday 15/10 13:15 - 15:00 | Lecture 13. R code. | Applied Bayesian modelling: Model choice and model checking. |
| Thursday 17/10 15:15 - 17:00 | Lecture 14. Presentation on age assessment paper. HeliCis paper. | Applications of Bayesian modelling. Examples. |
| Tuesday 22/10 13:15 - 15:00 |
Lecture 15 . Marin_etal_review. Marjoram_2003 . allcodes.zip |
Lecture 15: Approximate Bayesian Computation, or "What to do when exact Bayes is impossible?" (Lecturer: Umberto Picchini) |
| Thursday 24/10 15:15 - 17:00 | CANCELLED |
Recommended exercises
| Day | Exercises |
|---|---|
| Thursday 5/9 13:15 - 15:00 | After lecture 1: Make sure you can use R: Study A1 (i.e., Albert chapter 1) try Exercise 4; Study RC1, try Exercise 1.19. EXAM 2019-01-08 question 1. |
| Thursday 12/9 15:15-17:00 | After lectures 2-3: EXAMS: 2019-08-29 q1; 2019-01-08 q4; 2018-10-27 q1; 2018-01-02 q3; 2017-10-21 q1; Albert (mostly computations in R): A2.9: 1,4,5; A3.9: 1,3,4. A4.8 1,4,7. |
| Thursday 19/9 13:15 - 15:00 | After lectures 4-5: EXAMS: 2019-08-29 q2; 2019-01-08 q2; 2018-10-27 q2; 2018-01-02 q1, q2, q8; 2017-10-21 q2, q5; RC Exercises 2.11, 2.12, 2.18, 2.22, 3.13. |
| Thursday 26/9 15:15 - 17:00 | After lectures 6-7: EXAMS: 2019-08-29 q5; 2018-10-27 q3(except a); 2018-01-02 q5, q6; 2017-10-21 q4, q5. A5.13: Exercises 1, 4. A6.13: Exercises 2, 4. A7.12: Exercises 1, 2. A10.7: Exercises 1, 3. RC Exercises 6.7, 6.8, 7.11, 8.2, 8.8. |
| Thursday 3/10 15:15 - 17:00 | After lectures 8-9: EXAMS: 2019-08-29 q3 q6; 2019-01-08 q3, q6; 2018-07-10-21 q6. RC Exercises 5.8, 5.9, 5.10. |
| Thursday 10/10 13:15 - 15:00 | After lectures 10-11: EXAMS: 2019-08-29 q4 q8; 2019-01-08 q5; 2018-10-27 q4 q5 q6; 2018-01-02 q4 q7; 2017-10-21 q3. Extra exercises with solutions. |
| Thursday 17/10 13:15 - 15:00 | After lectures 12-13: EXAMS: 2019-08-29 q7; 2019-01-08 q7. A8.11: Exercises 1, 3. |
| Thursday 24/10 13:15 - 15:00 | CANCELLED |
Computer labs
To understand and learn the methods of this course, it is essential to work with examples on a computer. Our textbooks contain a large number of exercises, and recommended exercises are listed above.
As an obligatory part of the course, each student must do 3 assignments. The deadlines for these are 19 September, 3 October, and 17 October. Details about the assignments will be available here in Canvas. Answers must also be handed in via Canvas. Although students are welcome to cooperate in their work, each student must be prepared to explain orally all details of their own written answers.
The weekly computer labs will function as support for students, and an opportunity to get individual help with either exercises from the textbooks or with the assignments. Students choose and prioritize themselves what to work with, and how to work.
As all the course material uses the R language for examples and illustrations, students should also use this language. Students who are not familiar with this language need to study it individually during the first weeks of the course. See, for example, the introductory chapters of our textbooks.
Course summary:
| Date | Details | Due |
|---|---|---|