Markov chain Monte Carlo (MCMC) is a useful tool for generating samples fromcomplex distributions. Over the last few decades, MCMC has provided manysolutions to computing problems especially in Bayesian statistics. However,there are still some challenging problems such as local-trap or explorationof high dimensional space, which cannot be solved by a usual MCMC methodssuch as Gibbs sampling or the random walk (RW) MH algorithm. This workshopreviews the various advanced MCMC techniques with some illustrativeexamples. It is intended to serve researchers interested in Bayesian modeling or Monte Carlo simulation. It will most benefit those who already have a general familiarity with the concepts underpinning Bayesian analysis and/or the RW MH algorithm. Registration required; please click "More Information" below to access the registration form.