Latest news
Welcome to the course! The schedule for the course can be found in TimeEdit.
Teachers
Course coordinator, teacher, and lab supervisor: Petter Mostad
Course literature
Our required reading will be selected from three different
textbooks, all available online, and some occasional extra
material described in lecture notes. Specifically, we will use:
 (A) Albert: Bayesian Computation with R. Available as ebook; see also here. We cover (parts of) the chapters listed under "Program".
 (RC) Robert and Casella: Introducing Monte Carlo Methods
with R. Available
as ebook. Also available: Solutions
to oddnumbered exercises, and errata
/ additional
errata. We cover parts of the chapters listed under
"Program".
 (B) Bishop: Pattern Recognition And Machine Learning,
available here.
We cover parts of the chapters listed under "Program".
 Lecture notes for some of the course lectures will be made
available below. Occasionally they will contain additional
material not covered in the texts above.
Some additional reading, for the interested student:
 Gelman et al: Bayesian Data Analysis. Good book for learning about Bayesian theory and data analysis.
 Liu: Monte Carlo Strategies in Scientific Computing. Available as ebook.
 Johansen and Evers: Monte Carlo Methods. Available here.
 Robert and Casella: Monte Carlo Statistical Methods. Available as ebook.
 Gentle, Härdle and Mori: Handbook of Computational Statistics. Available as ebook.
EnglishSwedish mathematical dictionary
Program
Lectures
Rooms: Tuesday lectures are in Pascal, while Thursday
lectures are in Euler.
Day 
Literature (e.g., A2 means chapter 2 in Albert (see
above)) 
Contents 

Tuesday 4/9, 13:15  15:00, 
Lecture Notes. 
Lecture 1: Introduction. Motivation: Problems with
classical frequentist inference. Basic ideas of Bayesian
statistics. 
Thursday 6/9 13:15  15:00 
Lecture Notes. A2 (except
2.5), A3. B2 (2.1, 2.4.1, 2.4.2). 
Lecture 2: Basics ideas of conjugacy. Simple
computations. Mixtures. The exponential family of
distributions. 
Tuesday 11/9, 13:15  15:00 
Lecture Notes. A4.1  A4.4.
B2.3.1  B2.3.6 Rcode
examples 
Lecture 3: Some multivariate conjugacies. Bayesian
inference by discretization. Low dimensional inference. 
Thursday 13/9, 13:15  15:00 
Lecture Notes. A5.15.8. RC2,
RC3.12. B11.1.12. Rcode
examples 
Lecture 4: Inference by simulation. Monte Carlo
integration. Basic simulation methods. 
Tuesday 18/9, 13:15  15:00 
Lecture Notes. A67. RC6,7,8.
B11. 
Lecture 5: Introduction to Markov chain Monte Carlo
(MCMC) methods. 
Thursday 20/9, 13:15  15:00 
R code.
A67,10. RC6,7,8. B11. 
Lecture 6: More on MCMC. 
Tuesday 25/9, 13:15  15:00 
R code. Lecture Notes. A67. RC6,7,8.
B11. 
Lecture 7: Hierarchical models. Gibbs
sampling. 
Thursday 2/10, 13:15  15:00 
Lecture Notes.
R code. A67. RC6,7,8. B11. 
Lecture 8: Checking convergence. More on
MCMC simulations. 
Tuesday 4/10, 13:15  15:00 
Lecture Notes. R code. A67. RC6,7,8. B11.  Lecture 9: Extensions of
MetropolisHastings. More basic simulation methods. 
Thursday 28/9 13:15  15:00 
Lecture Notes.
R code. B9. RC5. 
Lecture 10: Some information theory. The
EM algorithm. 
Tuesday 9/10, 13:15  15:00 
Lecture Notes.
B8. 
Lecture 11: Introduction to Graphical
Models. 
Thursday 11/10, 13:15  15:00 
Lecture Notes.
R code. B8. 
Lecture 12: Inference for Graphical
Models. The ForwardBackward algorithm. 
Tuesday 16/10, 13:15  15:00 
Lecture Notes.
Rcode, Rcode,
Rcode. B13. 
Lecture 13: Inference for Graphical
Models. The Viterbi algorithm. The BaumWelch algorithm. 
Thursday 18/10, 13:15  15:00 
Lecture Notes.
A8. A study on medical age assessment. with Presentation 
Lecture 14: Model choice. Bayesian
modelling and Bayesian inference in practice. 
Tuesday 23/10, 13:15  15:00 
Lecture Notes.

Lecture 15: Some ways forward.
Variational Bayes. Approximate Bayesian Computation (ABC). 
Thursday 25/10, 13:15  15:00 
Lecture 16: Review. 
The final written exam is held Saturday 27 October 14:00 
18:00.
In addition to the lectures, you will work individually and in
groups on theoretical exercises and computer exercises. During
the course, Petter Mostad will be available every Thursday
15:15  17:00 in the computer room MVF22 (except 25
October) and Tuesdays 10:00  11:45 (except 25 September,
when the time is 15:15  17:00) in his office MVH3017 to
answer questions.
Recommended exercises:
Learning R: A (i.e., Albert) 1.6: Exercise 4, RC (i.e.,
Robert and Casella) Exercise 1.19
Oneparameter models: A2.9 Exercises 1,4,5; A3.9 Exercises
1,3,4.
Several parameters, discretization: A4.8 Exercises 1, 4, 7.
Simple simulation: RC Exercises 2.11, 2.12, 2.18, 2.22.
MCMC: 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
Simulation methods: RC Exercise 3.13
EM algorithm: RC Exercises 5.8, 5.9, 5.10.
Model comparison: A8.11: Exercises 1, 3
Inference for Markov chains: Extra exercises, with solutions.
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 20 September, 4
October, and 18 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 MVF22 15:15  17:00 every Thursday
starting 4/10 and ending 18/10.
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.
Assignments
See under Computer labs above.
Examination
To pass the course you need to
 have approved answers to the assignments. This is registered
as a separate 2 hp project in Ladok, so assignments can be
approved a different year from the final written exam.
 Pass the final written exam. No aids are allowed during the exam. Your grade for the course is based on the grade from the written exam. 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.
The final written exam will give a maximal score of 30 points.
The Chalmers grading scale is 1217.5: 3; 1823.5: 4; 2430: 5.
The GU grading scale is 1221.5: G; 2230: VG.
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 can do this from 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 reexamination:
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.
Course Evaluation
At the beginning of the course at least two students representatives from Chalmers and GU will be nominated. The teachers will meet the elected representatives 3 times over the period to keep track of how the course is going.Student representatives Chalmers:
Aditya Bhadravathi Sridhara
Agathe Gourrat
Isak Hjortgren
Sandra Jansson
Sharif Zahiri
Student representatives GU:
Sebastian Ahlman
Margareta Carlerös
Sören Richard
Zeinab Tir Sahar
Chengjie Wang
The largest change from the 2017 course edition is that the Bishop textbook has been added to the textbooks we use. This means that more of the required reading is covered in actual textbooks. Also, weekly office hours have been added to the schedule.
Old exams
Exam 20190829 with suggested solutions.
Exam 20190108 with suggested solutions.
Exam 20181027 with suggested solutions.
Exam 20180102 with suggested solutions.
Exam
20171021 with suggested
solutions.
=============================================
Recent exams in MVE186/MSA100:
Exam
20170605 (extra, irregularly scheduled exam) with suggested
solutions.
Exam
20170102, with suggested
solutions. You may skip question 8.
Exam
20161022, with suggested
solutions.
Some older exams in MVE186/MSA100:
Exam
20151024, with suggested
solutions: You may skip questions 2, 4, and possibly 6.
Exam
20150105. You may skip questions 1, 4, and 6, and
possibly 5.
Exam
20141027, with suggested solutions.
A
mock exam from 2014. You may skip question 2.
Some even older exams in MVE185/MSA100:
Exam
20091024, with suggested
solutions: You may skip question 7.
Exam
20081025, with suggested
solutions: You may skip question 2.