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Reports (Research Report) Year : 2008

Adaptive Importance Sampling in General Mixture Classes


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.
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Dates and versions

inria-00181474 , version 1 (24-10-2007)
inria-00181474 , version 2 (24-10-2007)
inria-00181474 , version 3 (25-10-2007)
inria-00181474 , version 4 (03-03-2008)


  • HAL Id : inria-00181474 , version 4


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