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arXiv · 0710.4242

Adaptive Importance Sampling in General Mixture Classes

Olivier Cappé·Randal Douc·Arnaud Guillin·Jean-Michel Marin·Christian P. Robert
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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.

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BibTeXRIS

Olivier Cappé, Randal Douc, Arnaud Guillin, Jean-Michel Marin, Christian P. Robert. 2008-05-30. Adaptive Importance Sampling in General Mixture Classes. https://doi.org/10.1007/s11222-008-9059-x

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