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.
Teachers
Student representatives Chalmers:
JOHN ERIKSSON
COSTANZA FRANCIONI
RICKARD JOHANSSON
BRITTA THÖRNBLOM
Student representatives GU:
ERIK HIRAD
REZAZADEH
ANDREA KROGDAL
ELIAS KAMYAB ORVAR
Course literature
Required reading:
- (A) Albert: Bayesian Computation with R. Available as e-book; see also here. This will be a main textbook. We will study Chapters 1-8 and 10.
- (RC) Robert and Casella: Introducing Monte Carlo Methods
with R. Available
as e-book. This book will also be a main textbook. Also
available: Solutions
to odd-numbered exercises, and errata
/ additional errata. We will
study most of this book, with some omissions in chapters 4, 5,
and 8: Details will be specified later.
- A text concerning computations for Bayesian Networks will be
made available via PingPong and GUL.
- 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 e-book.
- Johansen and Evers: Monte Carlo Methods. Available here.
- Robert and Casella: Monte Carlo Statistical Methods. Available as e-book.
- Gentle, Härdle and Mori: Handbook of Computational Statistics. Available as e-book.
Program
Lectures, always in Pascal (except Sept 7, when the room is
HC1)
| Day |
Chapters |
Contents |
|---|---|---|
| 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 |
A7 |
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 |
RC5 |
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)
| Day |
Exercises |
|---|---|
| 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.
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.
- 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.
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.