Subsampling Sequential Monte Carlo for Static Bayesian Models

David Gunawan, Khue-Dung Dang, Matias Quiroz (), Robert Kohn () and Minh-Ngoc Tran
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David Gunawan: School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).
Khue-Dung Dang: School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).
Matias Quiroz: School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical, Statistical Frontiers (ACEMS) and Research Division.
Minh-Ngoc Tran: ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and Discipline of Business Analytics, University

No 371, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)

Abstract: 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 and this is typically the most computation- ally 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 contributions. First, in order to speed up the SMC computation, we use an approximately unbiased and efficient annealed likelihood estimator based on data subsampling. The subsampling approach is more memory effi- cient than the corresponding full data SMC, which is an advantage for parallel computation. Second, we use a Metropolis within Gibbs kernel with two con- ditional updates. A Hamiltonian Monte Carlo update makes distant moves for the model parameters, and a block pseudo-marginal proposal is used for the particles corresponding to the auxiliary variables for the data subsampling. We demonstrate the usefulness of the methodology for estimating three gen- eralized linear models and a generalized additive model with large datasets.

Keywords: Hamiltonian Monte Carlo; Large datasets; Likelihood annealing (search for similar items in EconPapers)
JEL-codes: C11 C15 C55 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2019-04-01
New Economics Papers: this item is included in nep-cmp and nep-ecm
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