Title: Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems,
Abstract: Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that
are used to obtain random samples of a high dimensional random variable in a
sequential fashion. Many problems encountered in applications often involve
different types of constraints. These constraints can make the problem much
more challenging. In this paper, we formulate a general framework of using SMC
for constrained sampling problems based on forward and backward pilot
resampling strategies. We review some existing methods under the framework and
develop several new algorithms. It is noted that all information observed or
imposed on the underlying system can be viewed as constraints. Hence the
approach outlined in this paper can be useful in many applications.