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Adaptive Scan Gibbs Sampler for Large Scale Inference Problems

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 نشر من قبل Vadim Smolyakov
 تاريخ النشر 2018
  مجال البحث الاحصاء الرياضي
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For large scale on-line inference problems the update strategy is critical for performance. We derive an adaptive scan Gibbs sampler that optimizes the update frequency by selecting an optimum mini-batch size. We demonstrate performance of our adaptive batch-size Gibbs sampler by comparing it against the collapsed Gibbs sampler for Bayesian Lasso, Dirichlet Process Mixture Models (DPMM) and Latent Dirichlet Allocation (LDA) graphical models.



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