MVE188 / MSA102 Computational methods for Bayesian statistics Autumn 25
Course PM
This page contains the program for the course. Other information, such as learning outcomes, teachers, literature, examination, and old exams is in a separate course PM.
Program
The schedule is in TimeEdit and in the table below.
The program will be similar to last year, with some updates, that will be specified closer to the start of the course.
Schedule
The Schedule below is from 2024; the schedule for 2025 will be similar and details will be available closer to the start of the course.
| Contents | Study material / activity |
Time and place |
| Lecture 1: Introduction and preliminaries |
B2.1 - B2.4 (meaning chapter 2 sections 1 through 4 from Bishop; see literature list). A2, A3, A4 (chapters 2, 3, 4 from Albert). Some overheads. |
Monday 2/9, 15:15 - 17:00, Pascal. |
| Workshop / help |
A1, RC1. / Introduction to R for students who have not used R before. |
Wednesday 4/9, 8:00 - 9:45, MVF33. |
| Lecture 2: Using conjugacies in exponential families of distributions |
B2.1-B2.4. A2, A3, A4. Some overheads. |
Wednesday 4/9 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
Exam 2023-08-24 question 1; 2020-10-26q1; 2021-01-05q1; 2023-01-05q1; A1.6 exercise 4. A2.9; 1,4,5; A3.9; 1,3. R code. |
Thursday 5/9, 15:15 - 17:00, Pascal. |
|
Lecture 3: Conjugacies, mixtures, discretizations. Examples |
B2.1-B2.4. A2, A3, A4. Some overheads. R code. |
Monday 9/9, 15:15 - 17:00, Pascal. |
| Workshop / help |
Do task on overhead 11 of Lecture 3. Run through and complete remaining examples of Lecture 3. R code. In addition: Exercises of your choice from our textbooks. |
Wednesday 11/9, 8:00 - 9:45, MVF33. |
| Lecture 4: Inference by simulation. Monte Carlo integration. Basic simulation methods. Rejection sampling. |
B11.1 (until B11.1.6); A5; RC2, RC3. Some overheads. R code. |
Wednesday 11/9 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
Exercises: Based on student requests, we will go through exercises selected from lines 3 and 4 in the table of recommended study questions below. |
Thursday 12/9, 15:15 - 17:00, Pascal. |
| Lecture 5: Importance sampling and SIR. The multivariate Normal and Laplace approximation. Introduction to Markov chain Monte Carlo (MCMC) methods. |
B11.1.4 - B11.1.5. A5.9 - A5.10. B11.2.1 - B11.2.2. Some overheads. R code. |
Monday 16/9, 15:15 - 17:00, Pascal. |
| Workshop / help |
Clarifications based on incoming student questions. More on examples from lectures. Individual work/help with assignment questions. |
Wednesday 18/9, 8:00 - 9:45, MVF33. |
| Lecture 6: MCMC. Random walk. Independent proposal. Convergence; checking convergence. Burn-in. Smart proposals. |
A6. For information: RC6-8. Some overheads. R code. |
Wednesday 18/9 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
We will go through the exercises in lines 5 and 6 in the table of recommended study questions below, except those designated "extra". |
Thursday 19/9, 15:15 - 17:00, Pascal. |
| Lecture 7: More on basic MCMC. Graphical Models. |
B8 excluding 8.4.3-8.4.8. Some overheads. R code. |
Monday 23/9, 15:15 - 17:00, Pascal. |
| Workshop / help |
Individual or group work, with teacher available for help. |
Wednesday 25/9, 8:00 - 9:45, MVF33. |
| Lecture 8: Graphical models. Hierarchical models. Gibbs sampling. |
B11.3. A6. For information: RC6-8. Some overheads. R code. |
Wednesday 25/9 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
Students work in groups with the exercises specified in lines 7 and 8 in the table below (excluding those marked "extra"). Then we discuss the solutions together. |
Thursday 26/9, 15:15 - 17:00, Pascal. |
| Deadline first obligatory hand-in |
|
Friday 27/9 16:00. |
| Lecture 9: Hierarchical models. Hamiltonian MCMC. |
A7; RC7. B11.5. Some overheads. R code. |
Monday 30/9, 15:15 - 17:00, Pascal. |
| Workshop / help |
Individual or group work, with teacher available for help. |
Wednesday 2/10, 8:00 - 9:45, MVF33. |
| Lecture 10: Missing data / augmented data. Slice sampling. |
B11.4. Some overheads. R code. |
Wednesday 2/10 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
A discussion of common issues in assignment 1. Then, students work in groups with the exercises specified in lines 9 and 10 in the table below (excluding those marked "extra"). Then we discuss the solutions together. |
Thursday 3/10, 15:15 - 17:00, Pascal. |
|
Lecture 11: State space models and Hidden Markov Models. Kalman filters. |
Finishing overheads from Lecture 10. Then: Some overheads. R code. |
Monday 7/10, 15:15 - 17:00, Pascal. |
| Workshop / help |
Individual or group work, with teacher available for help. |
Wednesday 9/10, 8:00 - 9:45, MVF33. |
| Lecture 12: Kalman filters. Particle filters. Viterbi. |
S chapters 1,4,7. B1.6, 9.4 (9.2, 9.3); RC5.4. Some overheads. R code. |
Wednesday 9/10 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
Students work in groups with the exercises specified in lines 11 and 12 in the table below (excluding those marked "extra"). Then we discuss the solutions together. |
Thursday 10/10, 15:15 - 17:00, Pascal. |
| Deadline second obligatory hand-in |
|
Friday 11/10, 16:00. |
| Lecture 13: Some information theory. The EM algorithm. Parameter inference in state space models. |
B1.6, 9.4 (9.2, 9.3); RC5.4. Some overheads. R code. |
Monday 14/10, 15:15 - 17:00, Pascal. |
| Workshop / help |
Individual or group work, with teacher available for help. |
Wednesday 16/10, 8:00 - 9:45, MVF33. |
| Lecture 14: Variational Bayes. Applied Bayesian Modelling |
B10.1 (B10.2). Literature: A7, A8. Some overheads. R code. |
Wednesday 16/10 10:00 - 11:45, Pascal. |
| Exercise class (demonstration/discussion of exercises) |
Students work in groups with the exercises specified in lines 13 and 14 in the table below (excluding those marked "extra"). Then we discuss the solutions together. |
Thursday 17/10, 15:15 - 17:00, Pascal. |
| Lecture 15: Review |
Review. Some overheads. |
Monday 21/10, 15:15 - 17:00, Pascal. |
| WRITTEN EXAM |
Everything |
Saturday 26/10, 8:30 - 12:30. |
Recommended study questions
After the lecture whose number is on the left, you may work on the questions listed in that line.
| OLD EXAMS | Albert | Robert & Casella | Other | |
| 1 |
2023-08-24 question 1. |
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| 2 |
2023-01-05q1. 2021-01-05q1. 2020-10-26q1. 2019-01-08q1. |
A1.6 exercise 4. A2.9: 1,4,5; A3.9: 1,3 |
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| 3 | 2022-10-29q1. 2022-08-25q2. 2021-10-30q1. 2020-10-29q2. 2020-10-26q2. |
A3.9: 4; solutions |
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| 4 |
2022-10-29q2. 2021-08-26q2. 2019-08-29q1; 2019-01-08q4; 2018-10-27q1; 2018-01-02q3; 2017-10-21q1. |
A4.8 1,4,7. solutions |
RC 2.11, 2.12, 2.18, 2.22. |
|
| 5 |
2022-10-29q4. 2020-10-26q5. 2019-08-29q2. 2019-01-08q2ab. EXTRA: 2023-08-24q2. 2018-01-02q2. 2020-08-27q2. |
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| 6 |
2021-10-30q3. 2021-08-26q6. 2021-01-05q3. EXTRA: 2020-01-08q3. 2018-10-27q2. 2018-01-02q8. 2020-08-27q5. |
A5.13: 1,5. A6.13: 1,2. solutions | ||
| 7 |
2023-08-24q4. 2023-01-05q4, q7. 2022-08-25q4, q5. 2022-01-05q7. EXTRA: 2018-01-02q5. 2019-10-28q2. |
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| 8 |
EXTRA: 2021-10-30q6. 2021-08-26q4. 2021-01-05q5, q6. 2020-10-29q3, q6. 2020-10-26q7. 2020-08-27q3, q7; 2017-10-21q4, q5; 2020-01-08q5; 2019-10-28q4; 2019-08-29q3, q6; 2019-01-08q3, q6; 2018-10-27q5; 2018-01-02q6; 2017-10-21q6. |
A7.13: 1,2. |
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| 9 |
2020-10-26q3. 2023-01-05q2. 2022-10-29q3. 2022-08-25q1. 2021-10-30q2. |
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| 10 |
2022-01-05q2. 2021-01-05q2. |
A5.13: 2; A6.13: 3; A10.7: 6. A9.7: 3,4; solutions | ||
| 11 |
2023-01-05q3. 2022-08-25q3. 2021-10-30q4. 2020-10-29q5. 2020-10-26q4. |
A9.7: 6; A10.7: 5, 7. solutions | ||
| 12 |
2020-01-08q4. 2019-08-29q8. 2018-01-02q7. 2017-10-21q3. 2023-01-05q6. 2022-10-29q5. 2021-10-30q7. 2021-01-05q7. 2020-10-29q7. EXTRA: 2019-08-29q5. |
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| 13 |
2023-08-24q5. 2022-10-29q6. 2022-08-25q7. 2022-01-05q6. 2021-10-30q5. 2021-08-26q3. |
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| 14 |
2023-08-24q6. 2023-01-05q5. 2022-08-25q6. 2022-01-05q3, q8. 2021-08-26q5. 2020-10-26q6. |
Course work
To understand and learn the methods of this course, it is essential to work with examples on a computer, in addition to working with the study material and doing theoretical exercises. Our textbooks contain a large number of exercises, for both theoretical and computer solutions, and some exercises are listed above.
As an obligatory part of the course, each student must do 2 assignments. The deadlines for these are Friday 20 September 16:00 and Friday 11 October 16:00. Details about the assignments will be available from the links at the bottom of this page. 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.
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.
If you have problems with getting started with the course, or for example getting started with R, please do not hesitate to contact me. I hope that we can have active communication during the course. In addition to contact at lectures and exercise sessions, you may contact me on canvas or by mail at mostad@chalmers.se. I usually answer within a day or so.
Course summary:
| Date | Details | Due |
|---|---|---|