Jose Blanchet, Columbia University
Our focus is on the development of provably efficient simulation algorithms for estimating large deviations probabilities (such as overflow probabilities) in the context of many server queues. These types of systems, which have been the subject of much investigation in recent years, pose interesting challenges from a rare event simulation standpoint, given their measure valued state descriptor. We shall explain a technique that has the following elements. First, it introduces a pivotal set that is suitable chosen to deal with boundary-type behavior, which is common in the analysis of queueing systems. Second, it takes advantage of Central Limit Theorem approximations that have been developed recently for these types of systems and third it use a novel bridge-sampling approach in order to describe an symptotically optimal (in certain sense) importance sampling scheme. This work provides the first systematic approach to develop provably efficient rare-event simulation methodology for these types of systems.
This is a joint work with P. Glynn and H. Lam.
Jose Blanchet is a faculty member of the IEOR at Columbia University. Jose holds a Ph.D. in Management Science and Engineering from Stanford University. Prior to joining Columbia he was a faculty member in the Statistics Department at Harvard University. Jose is a recipient of the 2009 Best Publication Award given by the INFORMS Applied Probability Society and a CAREER award in Operations Research given by NSF in 2008. He worked as an analyst in Protego Financial Advisors, a leading investment bank in Mexico. He has research interests in applied probability and Monte Carlo methods. He serves in the editorial board of Advances in Applied Probability, Journal of Applied Probability, QUESTA and TOMACS.