Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems

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