A Survey of Bayesian Statistical Approaches for Big Data

TL;DR

This survey reviews Bayesian statistical methods tailored for Big Data, highlighting their benefits and discussing whether computational improvements alone suffice to tackle Big Data challenges.

Contribution

It provides a comprehensive overview of Bayesian approaches for Big Data and discusses their advantages and limitations in the context of current computational challenges.

Findings

Bayesian methods offer significant benefits for Big Data analysis.

Computational improvements are necessary but not sufficient for Big Data challenges.

The survey identifies key areas for future research in Bayesian Big Data methods.

Abstract

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data.