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A Survey of Bayesian Statistical Approaches for Big Data

  • Farzana JahanEmail author
  • Insha Ullah
  • Kerrie L. Mengersen
Chapter
Part of the Lecture Notes in Mathematics book series (LNM, volume 2259)

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 survey 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.

Keywords

Bayesian statistics Bayesian modelling Bayesian computation Scalable algorithms 

Notes

Acknowledgements

This research was supported by an ARC Australian Laureate Fellowship for project, Bayesian Learning for Decision Making in the Big Data Era under Grant no. FL150100150. The authors also acknowledge the support of the Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Farzana Jahan
    • 1
    Email author
  • Insha Ullah
    • 1
  • Kerrie L. Mengersen
    • 1
  1. 1.School of Mathematical Sciences, ARC Centre of Mathematical and Statistical Frontiers, Science and Engineering FacultyQueensland University of TechnologyBrisbaneAustralia

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