MCMC: Xiao-Li Meng

MCMC Title: The Magic of MCMC and Statistics: A Live Performance Abstract: Markov chain Monte Carlo (MCMC) methods, originated in computational physics more than half a century ago, have had magical impact in quantitative scientific investigations. This is mainly due to their ability to simulate from very complex distributions needed by all kinds of statistical models, from bioinformatics to financial engineering to astronomy. This talk provides an introductory tutorial of the two most frequently used MCMC algorithms: the Gibbs sampler and the Metropolis-Hastings algorithm. Using simple yet non-trivial examples, we demonstrate, via live performance, the good, bad, and ugly implementations. Along the way, we reveal the statistical thinking underlying their designs, including the secret behind the greatest statistical magic ... (Audience participations are required, though no prior experiences are needed.) [Guest lecture -- open to public -- in Kevin Rader's STAT 110 probability class.]