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Double-Parallel Monte Carlo for Bayesian analysis of big data
Ist Teil von
Statistics and computing, 2019-01, Vol.29 (1), p.23-32
Ort / Verlag
New York: Springer US
Erscheinungsjahr
2019
Link zum Volltext
Quelle
SpringerLink (Online service)
Beschreibungen/Notizen
This paper proposes a simple, practical, and efficient MCMC algorithm for Bayesian analysis of big data. The proposed algorithm suggests to divide the big dataset into some smaller subsets and provides a simple method to aggregate the subset posteriors to approximate the full data posterior. To further speed up computation, the proposed algorithm employs the population stochastic approximation Monte Carlo algorithm, a parallel MCMC algorithm, to simulate from each subset posterior. Since this algorithm consists of two levels of parallel, data parallel and simulation parallel, it is coined as “Double-Parallel Monte Carlo.” The validity of the proposed algorithm is justified mathematically and numerically.