Monte Carlo methods, particular Markov Chain Monte Carlo (MCMC), are now the methods of choice for making inferences about complex stochastic systems. Whilst MCMC dates back over 50 years, and there has been extensive research in Monte Carlo methods over the past 20 years, there are still many challenges that face researchers today.
These include attempting to analyse the highly complicated stochastic models and large scientific data sets that are now commonplace; and trying to understand the theoretical properties of some of the novel ideas that are proposed.
Currently, Monte Carlo methods are used by researchers in numerous scientific fields, including statistics, physics, engineering, genetics, econometrics, bioinformatics, and machine learning. This interdisciplinary workshop will bring together researchers from a variety of such fields to discuss current and novel Monte Carlo methodology, and to cross-fertilise ideas across these different disciplines. The workshop will have a broad focus, covering both recent advances in more established methods such as MCMC and sequential Monte Carlo, together with more recent ideas and ideas that have had little exposure within the statistics community, such as Variational methods, Population Monte Carlo, Approximate Bayesian Computing, Quasi Monte Carlo, and Indirect Inference.