Monte Carlo methods, with an emphasis on Bayesian computation


For whom

This course is intended mainly for Ph.D students, but also master's level students interested in the course are welcome.

The FGSS graduate school payes the travel expenses, accomodation and breakfasts and lunches during the course for the FGSS students. For others the course fee is 313 euros (check the details with Ari-Pekka Perkkiö) and the fee includes accomodation at Ahlmanin kartano, breakfasts and lunches.

If you are interested in participating in the course but are not student of FGSS, you can ask for FGSS funding from Ari-Pekka Perkkiö, <aperkkio at math dot hut dot fi>


In order to register for the course, send Ari-Pekka Perkkiö (see email address above) the following information, no later than on 7 May 2010.


The ideal student coming to this course has the following prerequisites.

However, I don't really expect that everybody will meet all of these expectations.

Getting the credit units

A student who successfully completes the practical work will get a certificate where we recommend that the course should be accepted as being worth 6 cu.

An exception is formed by those students who have completed the course Computational Statistics (Laskennallinen tilastotiede) at University of Helsinki, since there is significant overlap with that course. Those students cannot get the 6 cu from this course.

However, the credit units will be granted by the participant's own university. Therefore each student should negotiate with her or his thesis adviser on the question whether there are problems with this arrangement.

Contents of the course


The lectures give an overview of Monte Carlo methods which are useful especially for Bayesian inference. The planned topics are

Computer demos

The computer demos will include an introduction to R and an introduction to WinBUGS/OpenBUGS.

However, most of the time we will discuss assignments connected with the theory.

Practical work

In order to get the credits, the participants should complete the practical work assigned during the course.

Lecture material

Material for demos



Assignments for demos

Practical work


Deadline for the report: end of November, 2010.

Below are some suggestions for topics. The intention is that it should be possible to finish the project (from initial conception to a final report) within one week of concentrated work. If you come up with a more interesting topic, then feel free to suggest it.

Useful links


We will use R and WinBUGS/OpenBUGS during the course.

Every participant should install these programs on their computers before the course starts and bring the computer to the course.

R environment

R is a popular software environment for statistical computing and graphics. R is free, open source, and has lots of documentation available online. It is available for Windows, Mac OS X, and Linux. It can be downloaded from

We use (at least) the following packages

In the Windows version of R these can be installed from the menus. Try

Packages->Install package(s)...

select a location near you (e.g. Denmark or Sweden) and then select the name of the package you want from the list.


BUGS is a computing environment for doing Bayesian analysis with the aid of MCMC methods. There are two main versions available, namely WinBUGS and OpenBUGS. They can be installed by following the advice on the respective homepages:

Both of these programs are free, rather similar, and easy to install on a Windows computer according to the instructions.

If you have a Linux or a Macintosh computer, then it should be possible to run the Windows version of OpenBUGS using wine (which is a Windows emulator that you should install first), see the OpenBUGS homepage for more advice.

It may also be possible to run OpenBUGS directly under Linux, but then you don't get a graphical user interface but are limited to using scripts. In that case it may be possible to run OpenBUGS through R.

Other literature

Literature on R

The manuals or R can be read online.

There is lots of free documentation on R available through the subpage of R project homepage titled Contributed Documentation.

If you need an introduction to R in book form, I suggest one of the following

Monte Carlo methods and Bayesian computation

Introductory books on Monte Carlo methods in the context of Bayesian inference:

Books primarily on Bayesian statistics, which also discuss modern Bayesian computation:

A selection of more advanced books on Monte Carlo and/or Bayesian computation:

Last updated 2011-02-01 14:51
Petri Koistinen
petri.koistinen 'at'