Course syllabus

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.

I will continue with making the lectures available live and as a recording afterwards.  However, I hope most of you will attend the physical lectures.

You should study the reading material listed in the schedule below before each lecture. Active students with all kinds of questions are greatly appreciated.

On Thursdays there is a computer lab (in MVF22) on odd weeks and an exercise session (in MVF33) on even weeks. During computer labs, students work individually or in groups, with the computational or theoretical exercises they need to work on. The teacher is available for help and questions. During the exercise sessions we go through exercises together: The selection is based on student requests and the list of recommended exercises (see below). We also discuss solutions to the obligatory assignments. 

The second main part of the course is the obligatory assignments. There are two, and answers are submitted individually, although you are allowed to cooperate in small groups. The deadlines are in the third and sixth course weeks. Each assignment is based on material discussed at least one week before each deadline.

Guide for reading the textbooks:

  • The sections from Bishop selected in the specification of the study material below are relevant.
  • All chapters in Albert are relevant to some extent. Often concepts from the lectures are explained in more detail with good examples. However, concepts in Albert that do not appear at all in the lectures are not important for the course. Also, Albert has a lot of focus on using R functions from the R package LearnBayes (written by Albert) that implement various Bayesian computations. Using such pre-written R functions may be very helpful for some students but for others it obscures what is going on. In our course we will not focus on using these functions.
  • Chapters 2, 3, 6, 7 and section 5.4 of Robert and Casella are relevant. Again, there are many good examples, but concepts that only appear in these chapters and not in the lectures are not important for the course.

Schedule

Contents Study material
Time and place
Lecture 1: Introduction to the Bayesian paradigm. Issues with classical frequentist inference.

Literature: 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).

Make sure you know how to use R! See A1, or RC1, or any other introductory text to R. (RC means Robert and Casella)  

Overheads. RECORDING.

Monday 2023-08-28, 15:15-17:00, in Pascal. Zoom link (passcode 695751)
Lecture 2: Basic computations using conjugacy. The exponential family of distributions.

B2.1-B2.4. A2, A3, A4. Overheads. RECORDING.

Wednesday 2023-08-30, 10:00-11:45, in Euler. Zoom link (passcode 695751)
Computer lab Make sure you get started with using R, and understand the basics of R! See A1 or RC1 for basic exercises. Also, work on recommended exercises (see below).

Thursday 2023-08-31, 15:15-17:00, in MVF22.

Lecture 3: Using discretization. Mixtures. Some multivariate conjugacies.

B2.1-B2.4. A2, A3, A4. Overheads. RECORDING. R code.

Monday 2023-09-04, 15:15-17:00, in Pascal. Zoom link (passcode 695751)
Lecture 4: Inference by simulation. Monte Carlo integration. Basic simulation methods. Rejection sampling. More about priors.

B11.1 (until B11.1.6); B11.4; A5; RC2, RC3. Overheads. RECORDING. R code.

Wednesday 2023-09-06, 10:00-11:45, in ED. Zoom link (passcode 695751)
Exercise class Exercises from lines 2 and 3 (not old exams) in the of exercises below. Recording...such as it is Thursday 2023-09-07, 15:15-17:00, in MVF33.
Lecture 5: Importance sampling and SIR. The multivariate Normal and Laplace approximation. Introduction to Markov chain Monte Carlo (MCMC) methods.

B11.1.4-5; B11.2.1-2; A5.9-10. Overheads. RECORDING. R code

Monday 2023-09-11, 15:15-17:00, in Pascal. Zoom link (passcode 695751)
Lecture 6: MCMC. Random walk. Independent proposal. Convergence; checking convergence. Burn-in. Smart proposals.

A6. For information: RC6-8. Overheads. RECORDING. R code

Wednesday 2023-09-13, 10:00-11:45, in ED. Zoom link (passcode 695751)
Computer lab Individual and group work on recommended exercises. Thursday 2023-09-14, 15:15-17:00, in MVF22.
Deadline first assignment 

See below

Friday 2023-09-15, 16:00
Lecture 7: Hierarchical models. Tips and tricks. Convergence.

B11.3. A6. For information: RC6-8. Overheads. RECORDING. R code

Monday 2023-09-18, 15:15-17:00, in Pascal. Zoom link (passcode 695751)
Lecture 8: Hierarchical models. Gibbs sampling. Missing data / augmented data.

A7; RC7. Overheads. RECORDING. R code.

Wednesday 2023-09-20, 10:00-11:45, in ED. Zoom link (passcode 695751)

Exercise class Old exams, and exercises from Albert: See line 8 in the (updated) table below. Recording...such as it is

Thursday 2023-09-21, 15:15-17:00, in MVF33.

Lecture 9. Hamiltonian MCMC. State space models. Hidden Markov Models. Kalman

B11.5. Book by Särkkä: chapters 1,4,7. Overheads. RECORDING. R code.

Monday 2023-09-25, 15:15-17:00, in Pascal. Zoom link (passcode 695751)

Lecture 10: Kalman filters. Particle filters.

Book by Särkkä: chapters 1,4,7. Overheads. RECORDING. R code.

Wednesday 2023-09-27, 10:00-11:45, in ED.  Zoom link (passcode 695751)

Computer lab

Individual and group work on recommended exercises.

Thursday 2023-09-28, 15:15-17:00, in MVF22.

Lecture 11: Some information theory. The EM algorithm.       

B1.6, 9.4 (9.2, 9.3); RC5.4. Overheads. RECORDING. R code.

Monday 2023-10-02, 15:15-17:00, in Pascal. Zoom link (passcode 695751)

Lecture 12: Parameter inference in state space models.

Book by Särkkä: chapters 1,4,7. Overheads. RECORDING. R code.

Wednesday 2023-10-04, 10:00-11:45, in ED. Zoom link (passcode 695751)

Exercise class Discussion of the first assignment. Questions from old exams.

Thursday 2023-10-05, 15:15-17:00, in MVF33.

Deadline second assignment

See below

Friday 2023-10-06, 16:00

Lecture 13: Variational Bayes. A sampling example (slice sampling).

Literature:  B10.1 (B10.2). B11.4. Overheads. RECORDING. R code.

Monday 2023-10-09, 15:15-17:00, in Pascal. Zoom link (passcode 695751)

Lecture 14: Graphical Models.

B8 excluding 8.4.3-8.4.8. Overheads. RECORDING.

Wednesday 2023-10-11, 10:00-11:45, in EE. Zoom link (passcode 695751)

Computer lab Individual and group work on recommended exercises.

Thursday 2023-10-12, 15:15-17:00, in MVF22.  

Lecture 15: Finishing graphical models. Applied Bayesian modelling.

Literature: A7, A8. Overheads. RECORDING. R code.

Monday 2023-10-16, 15:15-17:00, in Pascal. Zoom link (passcode 695751)

Review

Everything: Please mail in requests for things to review.

Overheads. Overheads for example study. RECORDING.

Wednesday 2023-10-18, 10:00 - 11:45, in EE. Zoom link (passcode 695751)

Exercise class

Discussion of the second assignment. Please mail requests for questions we should go through.

Thursday 2023-10-19, 15:15-17:00, in MVF33. 

Written exam

Everything

Saturday 2023-10-28, 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.

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

some solutions

Some exercises 

solutions

3 2022-10-29q1. 2022-08-25q2. 2021-10-30q1. 2020-10-29q2. 2020-10-26q2.

A3.9: 4; solutions

Some exercises

solutions

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.

some solutions

5 2023-08-24q2. 2022-10-29q4. 2020-10-26q5. 2019-08-29q2; 2019-01-08q2ab, 2018-01-02q2, 2017-10-21q2. 2020-08-27q2.
6

2021-10-30q3; 2021-08-26q6; 2021-01-05q3; 2020-10-26q3; 2020-01-08q3; 2019-10-28q2; 2018-10-27q2; 2018-01-02q8; 2020-08-27q5; 2018-01-02q5.

A5.13: 1,5. A6.13: 1,2. solutions
7
8

2023-01-05q2. 2022-10-29q3. 2022-08-25q1. 2022-01-05q2. 2021-10-30q2. 2021-01-05q2.

A5.13: 2;  A6.13: 3;   A10.7: 6.  A9.7: 3,4; solutions

9

2023-01-05q3. 2022-08-25q3. 2022-01-05q5. 2021-10-30q4. 2020-10-29q5. 2020-10-26q4.

2019-08-29q5. 2020-01-08q4; 2019-08-29q8; 2018-01-02q7; 2017-10-21q3.

10
11

2023-08-24q5. 2022-10-29q6. 2022-08-25q7. 2022-01-05q6. 2021-10-30q5. 2021-08-26q3.

A9.7: 6; A10.7: 5, 7. solutions
12

2023-01-05q6. 2022-10-29q5. 2021-10-30q7. 2021-01-05q7. 2020-10-29q7.

13

2023-08-24q6. 2023-01-05q5. 2022-08-25q6. 2022-01-05q3, q8. 2021-08-26q5. 2020-10-26q6.

14

2023-08-24q4. 2023-01-05q4, q7. 2022-08-25q4, q5. 2022-01-05q7. 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. 
15

 

Back to the top

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 15 September 16:00 and Friday 6 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.

Back to the top

Course summary:

Date Details Due