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1 LTCI - Laboratoire Traitement et Communication de l-Information 2 CMAP - Centre de Mathématiques Appliquées 3 LATP - Laboratoire d-Analyse, Topologie, Probabilités 4 SELECT - Model selection in statistical learning Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d-Orsay, CNRS - Centre National de la Recherche Scientifique : UMR 5 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

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 importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are 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 sampling Adaptive Monte Carlo Mixture model Entropy Kullback-Leibler divergence EM algorithm Population Monte Carlo





Author: Olivier Cappé - Randal Douc - Arnaud Guillin - Jean-Michel Marin - Christian Robert -

Source: https://hal.archives-ouvertes.fr/



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