Statistics and Computing

, Volume 18, Issue 4, pp 447–459

Adaptive importance sampling in general mixture classes

Authors

  • Olivier Cappé
    • LTCITELECOM ParisTech, CNRS
  • Randal Douc
    • TELECOM SudParis
  • Arnaud Guillin
    • LATPEcole Centrale Marseille, CNRS
    • Project select, INRIA Saclay
    • CREST, INSEE
  • Christian P. Robert
    • CEREMADEUniversité Paris Dauphine, CNRS
    • CREST, INSEE
Article

DOI: 10.1007/s11222-008-9059-x

Cite this article as:
Cappé, O., Douc, R., Guillin, A. et al. Stat Comput (2008) 18: 447. doi:10.1007/s11222-008-9059-x

Abstract

In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion. The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.

Keywords

Importance samplingAdaptive Monte CarloMixture modelEntropyKullback-Leibler divergenceEM algorithmPopulation Monte Carlo

Copyright information

© Springer Science+Business Media, LLC 2008