  Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling
(SIR) algorithms are based on Importance Sampling (IS) and on some
resampling-based)rejuvenation algorithm which aims at fighting against weight
degeneracy. However %whichever the resampling technique used this mechanism
tends to be insufficient when applied to informative or high-dimensional
models. In this paper we revisit the rejuvenation mechanism and propose a class
of parameterized SIR-based solutions which enable to adjust the tradeoff
between computational cost and statistical performances.
