Bayes and big data: the consensus Monte Carlo algorithm

• Published in: International journal of management science and engineering management Vol. 11; no. 2; pp. 78 - 88
• Main Authors: Scott, Steven L., Blocker, Alexander W., Bonassi, Fernando V., Chipman, Hugh A., George, Edward I., McCulloch, Robert E.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02.04.2016
• Subjects: Bayesian inference; big data; distributed computing; embarrassingly parallel; Markov chain Monte Carlo
• ISSN: 1750-9653; 1750-9661
• DOI: 10.1080/17509653.2016.1142191

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).