MVE188 / MSA102 Computational methods for Bayesian statistics Autumn 2026

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. Lectures, on Mondays 15:15-17:00 and Wednesdays 10:00-11:45 in Pascal, will be traditional lectures, with students asking questions and some student activities. What to read before the lecture will be listed below, and overheads used will be available below (shortly) before each lecture. Wednesdays 8:00-9:45 in MVF33 will be exercise classes, with demonstrations and solutions of exercises in a list given below.

Study guide

The course PM contains a list of textbooks which you should use to prepare BEFORE each lecture. Rather than reading all of them, you should use material from the book or books that suit your background and style of learning. Murphy (denoted M below) is a quite advanced book, containing basically all material we go through and very much more. It may be useful as a reference. Bishop (B) is also advanced, but slightly easier to read. It also contains exercises, which Murphy does not. Albert (A) is an introductory book, with focus on practical Bayesian modelling and computation using R. It may be a useful learning tool for some. Robert & Casella (RC) and Särkää (S) may be referred to at some points, for specific material. 

The list of lectures below will contain, for each lecture, references to the relevant parts of the textbooks. It will also contain (posted shortly before the lecture) overheads and example code used. 

The lectures are not recorded. A few years back (mostly in connection with the pandemic) lectures were recorded. Here is a list of recordings from 2023; note however that the sequence of subjects has changed, even if the overall contents is mostly the same. 

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 below.

As an obligatory part of the course, each student must do 2 assignments. The deadlines for these are Tuesday 15 September at 23:59 and Friday 9 October at 23:59. Details about the assignments will be available from the links at the bottom of this page. Answers must be handed in via Canvas. It is important that you use the assignments to learn how to model and formulate computational solutions for practical problems. These skills will be tested in obligatory workshops after your solutions have been handed in. For each of the two assignments you will choose one of four possible times for a workshop to attend. There you will, without any aids, work in groups on questions related to the assignments, and present your results orally at the end. 

As much of 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.

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Schedule

Recommended study questions, in addition to those listed for exercise sessions.  

THIS TABLE IS PRELIMINARY AND WILL BE UPDATED. Each line contains exercises you should be able to do after studying the material for the lecture with the number at the beginning of the line. 

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