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Journal Articles Statistics and Computing Year : 2008

Adaptive Importance Sampling in General Mixture Classes

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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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Dates and versions

hal-00180669 , version 1 (19-10-2007)
hal-00180669 , version 2 (27-02-2008)
hal-00180669 , version 3 (30-04-2008)
hal-00180669 , version 4 (30-05-2008)

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Cite

Olivier Cappé, Randal Douc, Arnaud Guillin, Jean-Michel Marin, Christian P. Robert. Adaptive Importance Sampling in General Mixture Classes. Statistics and Computing, 2008, 18 (4), pp.447-459. ⟨10.1007/s11222-008-9059-x⟩. ⟨hal-00180669v4⟩
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