Period 3: theoretical background for understanding Bayes formula and its use in statistical analysis. Introduction to random variables and conditional expectations..
Period 4: practical skills for computerized Bayesian analysis using Monte Carlo Markov chain methods is given using Matlab.
Information on the lecturer´s research interests can be found here.
Lectures are given in Finnish or English depending on the audience.
Lecturer´s office hour Thursday 13-14 (room TD 321).
10-11 Chapter II§1, Sami Tiainen
11-12 Chapter II§2 (Borel sets), Matti Saarela
14-15 Chapter II§3 (Shorten!), Emilia Ylirinne
15-16 Chapter II§4, Christian Rapold
10-11 Chapter II§7 (until 7 on page 225), Gernot Härzel
11-12 Chapter II§7 (remaining part), Henri Pesonen
10-11 Jussi-Pekka Penttinen (Ergodic Theorem)
11-12 Timo Pylvänäinen (Theory of Markov chains)
There will be a set of lectures (including one presentation by a student) about Markov chain Monte Carlo (MCMC) methods, that will be useful in the project works.
In the remaining lecture time slots the lecturer is available for discussions about the project works. Please agree on meeting times beforehand by email.
Wednesday February 7 10-12: assignment of project work topics.
Thursday February 8 10-12: Lecture on MCMC methods
Wednesday February 14 10-11: Lighthouse (Matti, Henri, Emilia, Sami)
Wednesday February 14 11-12: Rounded data (Jussi-Pekka)
Thursday February 15 10-11: Coarse data (Gernot, Christian)
Thursday February 15 11-12: Analysis of Proportions (Timo)
Wednesday March 7 10-11: Lighthouse group meeting