Brazilian Journal of Probability and Statistics

Comparing consensus Monte Carlo strategies for distributed Bayesian computation

Steven L. Scott

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Abstract

Consensus Monte Carlo is an algorithm for conducting Monte Carlo based Bayesian inference on large data sets distributed across many worker machines in a data center. The algorithm operates by running a separate Monte Carlo algorithm on each worker machine, which only sees a portion of the full data set. The worker-level posterior samples are then combined to form a Monte Carlo approximation to the full posterior distribution based on the complete data set. We compare several methods of carrying out the combination, including a new method based on approximating worker-level simulations using a mixture of multivariate Gaussian distributions. We find that resampling and kernel density based methods break down after 10 or sometimes fewer dimensions, while the new mixture-based approach works well, but the necessary mixture models take too long to fit.

Article information

Source
Braz. J. Probab. Stat., Volume 31, Number 4 (2017), 668-685.

Dates
Received: December 2016
Accepted: April 2017
First available in Project Euclid: 15 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1513328760

Digital Object Identifier
doi:10.1214/17-BJPS365

Mathematical Reviews number (MathSciNet)
MR3738171

Zentralblatt MATH identifier
1385.65008

Keywords
Big data cloud computing Bayesian modeling

Citation

Scott, Steven L. Comparing consensus Monte Carlo strategies for distributed Bayesian computation. Braz. J. Probab. Stat. 31 (2017), no. 4, 668--685. doi:10.1214/17-BJPS365. https://projecteuclid.org/euclid.bjps/1513328760


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