Speaker
Jan Hamann
(Unknown)
Description
In the field of cosmology, the analysis of observational data with Bayesian methods is wide-spread. Only fairly recently it was suggested to apply these methods also to particle physics, to constrain the free parameters of supersymmetric models such as the CMSSM. The technical challenge of these analyses lies in the need to accurately sample a multi-dimensional parameter space in a reasonable amount of time. Markov Chain Monte Carlo (MCMC) algorithms offer an elegant solution to this problem. I will give a brief introduction to the theory of MCMC and illustrate its applications with a few examples from the recent literature.
