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Adaptive Importance Sampling in General Mixture Classes

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 نشر من قبل Olivier Cappe
 تاريخ النشر 2008
  مجال البحث الاحصاء الرياضي
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 تأليف Olivier Cappe




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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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