Efficient Monte Carlo sampling by
Jonathan Weare, CIMS
Monte Carlo sampling methods often suffer from long correlation times.
Consequently, these methods must be run for many steps to generate an
independent sample. In this talk a method is proposed to overcome
this difficulty. The method utilizes information from rapidly
equilibrating coarse Markov chains that sample marginal distributions
of the full system. This is accomplished through exchanges
between the full chain and the auxiliary coarse chains. Results of
numerical tests on the bridge sampling and filtering/smoothing problems
for a stochastic differential equation are presented. If time
permits we will discuss applications of the method to a filtering
problem for a bimodal model of the Kuroshio current.