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Population Monte Carlo (PMC) sampling methods are powerful tools forapproximating distributions of static unknowns given a set of observations.These methods are iterative in nature: at each step they generate samples froma proposal distribution and assign them weights according to the importancesampling principle. Critical issues in applying PMC methods are the choice ofthe generating functions for the samples and the avoidance of the sampledegeneracy. In this paper, we propose three new schemes that considerablyimprove the performance of the original PMC formulation by allowing for betterexploration of the space of unknowns and by selecting more adequately thesurviving samples. A theoretical analysis is performed, proving the superiorityof the novel schemes in terms of variance of the associated estimators andpreservation of the sample diversity. Furthermore, we show that they outperformother state of the art algorithms (both in terms of mean square error androbustness w.r.t. initialization) through extensive numerical simulations.

Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes