Adaptive Importance Sampling in General Mixture Classes
Year: 2007
Type: preprint
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.
Institutions: Centre National de la Recherche Scientifique, ParisTech, Laboratoire Traitement et Communication de l’Information, Télécom SudParis, Institut de Mathématiques de Marseille, Centrale Méditerranée, Institut national de recherche en sciences et technologies du numérique, Centre Inria de Saclay, Centre de Recherche en Mathématiques de la Décision, Université Paris Dauphine-PSL
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3103126047