parallelMCMCcombine: an R package for bayesian methods for big data and analytics.

PLoS One

Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, United States of America.

Published: December 2015

AI Article Synopsis

  • * The R package parallelMCMCcombine is introduced, which implements techniques to combine independent posterior samples obtained from these subsets, specifically using Bayesian models like logistic regression and Gaussian mixture models.
  • * The package is particularly useful for researchers dealing with unknown parameters in fixed-dimension continuous spaces, facilitating the exploration of various methods to enhance research in this fast-evolving field.

Article Abstract

Recent advances in big data and analytics research have provided a wealth of large data sets that are too big to be analyzed in their entirety, due to restrictions on computer memory or storage size. New Bayesian methods have been developed for data sets that are large only due to large sample sizes. These methods partition big data sets into subsets and perform independent Bayesian Markov chain Monte Carlo analyses on the subsets. The methods then combine the independent subset posterior samples to estimate a posterior density given the full data set. These approaches were shown to be effective for Bayesian models including logistic regression models, Gaussian mixture models and hierarchical models. Here, we introduce the R package parallelMCMCcombine which carries out four of these techniques for combining independent subset posterior samples. We illustrate each of the methods using a Bayesian logistic regression model for simulation data and a Bayesian Gamma model for real data; we also demonstrate features and capabilities of the R package. The package assumes the user has carried out the Bayesian analysis and has produced the independent subposterior samples outside of the package. The methods are primarily suited to models with unknown parameters of fixed dimension that exist in continuous parameter spaces. We envision this tool will allow researchers to explore the various methods for their specific applications and will assist future progress in this rapidly developing field.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4178156PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0108425PLOS

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