Adaptive Importance Sampling in General Mixture Classes

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.
Type de document :
[Research Report] RR-6332, INRIA. 2008
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger
Contributeur : Jean-Michel Marin <>
Soumis le : lundi 3 mars 2008 - 14:19:56
Dernière modification le : mercredi 21 novembre 2018 - 16:12:02
Document(s) archivé(s) le : vendredi 25 novembre 2016 - 22:22:10


Fichiers produits par l'(les) auteur(s)


  • HAL Id : inria-00181474, version 4


Olivier Cappé, Randal Douc, Arnaud Guillin, Jean-Michel Marin, Christian Robert. Adaptive Importance Sampling in General Mixture Classes. [Research Report] RR-6332, INRIA. 2008. 〈inria-00181474v4〉



Consultations de la notice


Téléchargements de fichiers