Title	Double-Parallel Monte Carlo for Bayesian Analysis of Big Data
Creator	Jia, Bochao
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-05-21T12:33
Description	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 (Pop-SAMC) 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 â Double Parallel Monte Carloâ . The validity of the proposed algorithm is justified mathematically and numerically.
Extent	40.0
Subject	Mathematics; Statistics; Numerical analysis
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Florida
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2019-03-20
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0377194
URI	http://hdl.handle.net/2429/68964
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Graduate
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Double-Parallel Monte Carlo for Bayesian Analysis of Big Data Jia, Bochao

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