Journal article

Subsampling sequential Monte Carlo for static Bayesian models

D Gunawan, KD Dang, M Quiroz, R Kohn, MN Tran

Statistics and Computing | Springer | Published : 2020


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 posterior distribution. Each update of the particle cloud consists of three steps: reweighting, resampling, and moving. In the move step, each particle is moved using a Markov kernel; this is typically the most computationally expensive part, particularly when the dataset is large. It is crucial to have an efficient move step to ensure particle diversity. Our article makes two important contributio..

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University of Melbourne Researchers


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