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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.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-01566898
Contributor : Sophie Donnet Connect in order to contact the contributor
Submitted on : Friday, July 21, 2017 - 1:44:11 PM
Last modification on : Wednesday, June 16, 2021 - 3:52:19 AM

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DoR17-StCo.pdf
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Identifiers

  • HAL Id : hal-01566898, version 1
  • ARXIV : 1707.07971

Citation

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

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