Markov chain Monte Carlo methods, sequential Monte Carlo methods, and other related stochastic algorithms have become widely used tools in many application fields of mathematics. Despite their massive use, the theoretical understanding of convergence properties of these algorithms is often rather rudimentary. This is in particular the case in high dimensional models that typically arise in many applications. Whereas formerly, research has often been carried out more or less independently in probability theory, stochastic analysis, and statistical mechanics on the one side, and theoretical computer science, discrete mathematics, and numerical analysis on the other side, recently there is a rapidly growing activity at the borderline of the different disciplines. Besides classical probabilistic techniques (e.g. martingale methods), concepts from statistical mechanics (phase transitions, critical slowing down), and techniques from stochastic analysis (decay to equilibrium of Markov processes, spectral gap estimates) as well as infinite dimensional analysis (e.g. logarithmic Sobolev inequalities) have become crucial.

Auszug zu diesem Minisymposium aus dem Programmheft (Stand: 15. Juli 2006). Weitere nützliche Informationen rund um die Tagung können der verkürzten Ausgabe des Programmheftes entnommen werden.

Programm (Stand: 07.09.2006):