MVE187/MSA101, Computational methods for Bayesian statistics, 2017/18

Latest news

Re-exam 2018-01-02 with suggested solutions.

Exam 2017-10-21 with suggested solutions.

Welcome to the course! The schedule for the course can be found in TimeEdit.

The contents of the course will be quite similar to the previous course MVE186/MSA100, with the changes that the few non-Bayesian items have been removed and some of the Bayesian items will be treated in somewhat more depth. Please see last year's home page for MVE186/MSA100 for more information.

Formally, and in terms of registration and being part of programs, MVE187 should function just like MVE186 did for Chalmers students, and MSA101 should function just like MSA100 for GU students.


Petter Mostad

Student representatives Chalmers:

Student representatives GU:

Course literature

Required reading:

Some additional reading, for the interested student:


Lectures, always in Pascal (except Sept 7, when the room is HC1)

Tuesday 29/8, 13:15 - 15:00
A1, RC1, Lecture Notes
Lecture 1: Introduction. Motivation: Problems with classical frequentist inference. The R language.
Thursday 31/8 13:15 - 15:00
A2, A3, Lecture Notes Lecture 2: Basics of Bayesian inference. Conjugacy. Discretization.
Tuesday 5/9, 13:15 - 15:00
A4, A5, Lecture Notes Lecture 3: The Bayesian inference paradigm.
Thursday 7/9, 13:15 - 15:00
A5, RC2, Lecture Notes Lecture 4: Basics of sample simulation. NOTE: The room is HC1.
Tuesday 12/9, 13:15 - 15:00
A6, RC3, Lecture Notes Lecture 5: Introduction to Markov chain Monte Carlo (MCMC) methods.
Thursday 14/9, 13:15 - 15:00
Lecture 6: Hierarchical modelling. NOTE: Guest lecturer: Ivar Simonsson.
Tuesday 19/9, 13:15 - 15:00
A6, RC6
Lecture 7: MCMC 
Thursday 21/9, 13:15 - 15:00
A10, RC7
Lecture 8: Gibbs sampling
Tuesday 26/9, 13:15 - 15:00
(RC4), Lecture Notes
Lecture 9: More on simulation, and convergence.
Thursday 28/9 13:15 - 15:00
Lecture 10: Optimization: The EM algorithm. Simulated annealing. 
Tuesday 3/10, 13:15 - 15:00
Lecture Notes (ref. mat. in PingPong/GUL)
Lecture 11: Introduction to Bayesian Networks
Thursday 5/10, 13:15 - 15:00
Lecture notes (ref. mat. in PingPong/GUL)
Lecture 12: Computational methods for Bayesian Networks.
Tuesday 10/10, 13:15 - 15:00
RC8, Lecture Notes
Lecture 13: Monitoring convergence + misc. mychallenge.R
Thursday 12/10, 13:15 - 15:00
A8, Lecture Notes
Lecture 14: Model choice / model checking.
Tuesday 17/10, 13:15 - 15:00
Lecture Notes
Lecture 15: A small taste of further methods: ABC, variants of MCMC, INLA, etc.
Old exam from 2016-10-22
Thursday 19/10, 13:15 - 15:00

Lecture 16: Review. Old exams from 2017-01-02 and 2017-06-05

Recommended exercises (preliminary list)

Tuesday 29/8, 13:15 - 15:00 Make sure you have enough knowledge about R.     A1.6: Exercise 4.     RC Exercise 1.19
Thursday 31/8 13:15 - 15:00 A2.9 Exercises 1, 4, 5.     A3.9 Exercises 1, 3, 4.
Tuesday 5/9, 13:15 - 15:00 A4.8 Exercises 1, 4, 7.
Thursday 7/9, 13:15 - 15:00 RC Exercises 2.11, 2.12, 2.18, 2.22.      A5.13 Exercises 1, 4. 
Tuesday 12/9, 13:15 - 15:00 A6.13 Exercises 2,4.      RC Exercise 3.13
Thursday 14/9, 13:15 - 15:00 Deadline for assignment 1!      A7.13 Exercises 1, 2
Tuesday 19/9, 13:15 - 15:00 RC Exercises 6.7, 6.8
Thursday 21/9, 13:15 - 15:00 A10.7 Exercises 1,3      RC Exercise 7.11
Tuesday 26/9, 13:15 - 15:00 RC Exercises 8.1, 8.2
Thursday 28/9 13:15 - 15:00 Deadline for assignment 2!    A8.11 Exercises 1, 3
Tuesday 3/10, 13:15 - 15:00
Thursday 5/10, 13:15 - 15:00 Extra exercises with suggested solutions
Tuesday 10/10, 13:15 - 15:00 RC Exercise 8.8
Thursday 12/10, 13:15 - 15:00 Deadline for assignment 3!
Tuesday 17/10, 13:15 - 15:00
Thursday 19/10, 13:15 - 15:00

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 will be listed above.

As an obligatory part of the course, each student must do 3 assignments. The deadlines for these are 14 September, 28 September, and 12 October. Details about the assignments will be available via PingPong for Chalmers students and GUL for GU students. Answers must also be handed in via PingPong/GUL. 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. The computer labs are held in MVF25 15:15 - 17:00 every Thursday starting 31/8 and ending 12/10. Note that on Thursday 14/9, the teacher will be available only via mail/chat.

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.

NOTE: In addition to the computer lab times listed above, I will be in my office MVH3017, answering questions, at the following times:
Tuesday 10/10 15:15 - 17:00
Tuesday 17/10 15:15 - 17:00
Thursday 19/10 15:15 - 17:00

Course requirements

The learning goals of the course can be found in the course plan. To paraphrase, the goal is to give students a firm understanding of the principles of Bayesian inference and how they differ from frequentist inference principles, as well as a good technical capability for making such computational inference in a range of models of medium complexity.


See under Computer labs above.


To pass the course you need to

The exam will contain questions asking you to describe/explain/prove theory, and questions asking you to apply theory to specific situations, to obtain specific equations or computational algorithms. Examples of questions from previous exams and practice exams that are relevant for you will be given under "Old exams" below. 

Examination procedures

In Chalmers Student Portal you can read about when exams are given and what rules apply on exams at Chalmers. In addition to that, there is a schedule when exams are given for courses at University of Gothenburg.

Before the exam, it is important that you sign up for the examination. If you study at Chalmers, you will do this by the Chalmers Student Portal, and if you study at University of Gothenburg, you sign up via GU's Student Portal.

At the exam, you should be able to show valid identification.

After the exam has been graded, you can see your results in Ladok by logging on to your Student portal.

At the annual (regular) examination:
When it is practical, a separate review is arranged. The date of the review will be announced here on the course homepage. Anyone who can not participate in the review may thereafter retrieve and review their exam at the Mathematical Sciences Student office. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

At re-examination:
Exams are reviewed and retrieved at the Mathematical Sciences Student office. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

Old exams

Exam 2017-10-21 with suggested solutions.

Recent exams in MVE186/MSA100:

Exam 2017-06-05 (extra, irregularly scheduled exam) with suggested solutions.

Exam 2017-01-02, with suggested solutions. You may skip question 8.

Exam 2016-10-22, with suggested solutions.

Some older exams in MVE186/MSA100:

Exam 2015-10-24, with suggested solutions: You may skip questions 2, 4, and possibly 6.

Exam 2015-01-05. You may skip questions 1, 4, and 6, and possibly 5. 

Exam 2014-10-27, with suggested solutions.

A mock exam from 2014. You may skip question 2.

Some even older exams in MVE185/MSA100:

Exam 2009-10-24, with suggested solutions: You may skip question 7.

Exam 2008-10-25, with suggested solutions: You may skip question 2.