Subsampling sequential Monte Carlo for static Bayesian models

We show how to speed up sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the p...

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Veröffentlicht in:	Statistics and computing 2020-11, Vol.30 (6), p.1741-1758
Hauptverfasser:	Gunawan, David, Dang, Khue-Dung, Quiroz, Matias, Kohn, Robert, Tran, Minh-Ngoc
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Bayesian analysis Datasets Generalized linear models Kernels Mathematics and Statistics Parallel processing Probability and Statistics in Computer Science Resampling Statistical inference Statistical models Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs
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