Bayes and big data: the consensus Monte Carlo algorithm. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Scott, Steven L.; Blocker, Alexander W.; Bonassi, Fernando V.; Chipman, Hugh A.; George, Edward I.; McCulloch, Robert E.
• Is Part Of: International journal of management science and engineering management. Volume 11:Issue 2(2016)
• Page Start: 78
• Page End: 88
• Publication Date: 2016-04-02
• Subjects: Bayesian inference -- Markov chain Monte Carlo -- distributed computing -- big data -- embarrassingly parallel; C1 -- C4 -- C6 -- C8; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2016.1142191
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract : A useful definition of 'big data' is data that is too big to process comfortably on a single machine, either because of processor, memory, or disk bottlenecks. Graphics processing units can alleviate the processor bottleneck, but memory or disk bottlenecks can only be eliminated by splitting data across multiple machines. Communication between large numbers of machines is expensive (regardless of the amount of data being communicated), so there is a need for algorithms that perform distributed approximate Bayesian analyses with minimal communication. Consensus Monte Carlo operates by running a separate Monte Carlo algorithm on each machine, and then averaging individual Monte Carlo draws across machines. Depending on the model, the resulting draws can be nearly indistinguishable from the draws that would have been obtained by running a single-machine algorithm for a very long time. Examples of consensus Monte Carlo are shown for simple models where single-machine solutions are available, for large single-layer hierarchical models, and for Bayesian additive regression trees (BART).