parallelMCMCcombine: An R Package for Bayesian Methods for Big Data and Analytics

TL;DR

The paper introduces the R package parallelMCMCcombine, which implements methods for combining independent subset posterior samples in Bayesian analysis of large datasets, facilitating scalable inference.

Contribution

It provides an accessible R package implementing four techniques for combining subset posteriors, advancing practical Bayesian analysis for big data.

Findings

Effective for logistic regression, Gaussian mixture, and hierarchical models

Demonstrated on simulation and real data examples

Supports models with fixed-dimensional parameters in continuous spaces

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 large data sets that are only large 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.