The Markov chain Monte Carlo method - an approach to (randomized) approximate counting

Markov chain simulation to obtain its samples. (Reminder: Monte Carlo
algorithms are a method for approximate integration or counting. They are
founded on the observation that the relative frequency of samples taken from a
sample space can be used to approximate the quantity of interest.)

Markov chain Monte Carlo algorithms have been widely used in statistical
physics where obtaining the necessary samples directly can be a non-trivial
task. However, only in the last 15 years have tools emerged that allow a
rigorous perfomance analysis of such algorithms. The focus of the talk will be
to give a description of some of the tools available.