Diagnostics for Inexact Monte Carlo
Many statistical problems involve approximating expectations with respect to a given "target" distribution. Markov chain Monte Carlo algorithms produce asymptotically exact approximations, meaning the Markov chain's stationary distribution is identical to the target distribution. Asymptotically inexact algorithms generate sequences without this property; even asymptotically the samples generated only follow the target distribution approximately. For many challenging problems, the only feasible approximations are based on asymptotically inexact algorithms. I will describe novel diagnostic tools for analyzing the output from both asymptotically exact and asymptotically inexact Monte Carlo methods. (This research is joint with Bokgyeong Kang and John Hughes.)
Murali Haran is Professor and Head of the Department of Statistics at Penn State University. He has a PhD in Statistics from the University of Minnesota, and a BS in Computer Science (with minors in Statistics, Mathematics and Film Studies) from Carnegie Mellon University. His research interests are in Monte Carlo algorithms, spatial models, statistical analysis of complex computer models, and interdisciplinary research in climate science and infectious disease modeling. He is a Fellow of the American Statistical Association and received the 2015 Young Researcher Award from The International Environmetrics Society to "recognize and honor outstanding contributions to the field of environmetrics."