Fall Seminar Series - September 15, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
Stochastic Approximation in Monte Carlo Computation
Faming Liang
Department of Statistics
Texas A & M University
Thursday, September 15, 2005
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
The
Wang-Landau algorithm (Wang and Landau, 2001) has received much
attention in the literature recently, although the lack of a rigorous
theory on its convergence has been a concern. In this talk, I will
present the stochastic approximation Monte Carlo (SAMC) algorithm,
which
can be regarded as a stochastic approximation extension of the
Wang-Landau algorithm. The convergence of the algorithm will also be
addressed. SAMC represents a new development of the stochastic
approximation method (Robbins and Monro, 1951), extending the
applications of stochastic approximation to Monte Carlo
computation. The numerical results on importance sampling, model
selection, and Monte Carlo optimization indicate that with an
appropriate setting, SAMC outperforms the methods conventionally used
for these problems. This talk is partially based on a joint work with
Chuanhai Liu and Raymond J. Carroll.