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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.
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Contributor : Jean-Michel Marin <>
Submitted on : Monday, March 3, 2008 - 2:19:56 PM
Last modification on : Friday, November 6, 2020 - 11:36:04 PM
Long-term archiving on: : Friday, November 25, 2016 - 10:22:10 PM


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  • 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⟩



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