Computational Methods for Bayesian Statistics
About
The course gives an introduction to Bayesian statistical modelling and inference, focusing on computational methods such as Markov chain Monte Carlo (MCMC) and other simulation methods, but also looking at tools such the EM algorithm. We emphasize the interplay between statistical modelling and applied problem solving, as well as computational and theoretical aspects of the models.
Prerequisites and selection
Entry requirements
Basic skills in mathematical statistics.
Basic skills in scientific programming (for example in Matlab or R) as achieved by completing MSG400 "Stochastic Data Processing and Simulation".
Selection
All eligible applicants who have applied before the deadline will be granted a place.
Facilities
Mathematical 91̽»¨s is a joint department of Chalmers/University 91̽»¨. Your education takes place in the spacious and bright premises of Mathematical 91̽»¨s at the Chalmers campus Johanneberg, where there are lecture halls, computer rooms and group rooms. Here you can also find student lunch room and reading room, as well as student counsellors and student office.
