Shortened Bridge Sampler: Using deterministic approximations to accelerate SMC for posterior sampling

Abstract : Sequential Monte Carlo has become a standard tool for Bayesian Inference of complex models. This approach can be computationally demanding, especially when initialized from the prior distribution. On the other hand, deter-ministic approximations of the posterior distribution are often available with no theoretical guaranties. We propose a bridge sampling scheme starting from such a deterministic approximation of the posterior distribution and targeting the true one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence of distributions that is determined in an adaptive way. We illustrate the robustness and the efficiency of the methodology on a large simulation study. When applied to network datasets, SBS inference leads to different statistical conclusions from the one supplied by the standard variational Bayes approximation.
Type de document :
Pré-publication, Document de travail
2017
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https://hal.archives-ouvertes.fr/hal-01566898
Contributeur : Sophie Donnet <>
Soumis le : vendredi 21 juillet 2017 - 13:44:11
Dernière modification le : jeudi 27 juillet 2017 - 01:09:51

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DoR17-StCo.pdf
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  • HAL Id : hal-01566898, version 1
  • ARXIV : 1707.07971

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Sophie Donnet, Stéphane Robin. Shortened Bridge Sampler: Using deterministic approximations to accelerate SMC for posterior sampling. 2017. <hal-01566898>

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