@inbook{724b472ca1e04c609799c5a44cec7f82,
title = "A Survey of Bayesian Statistical Approaches for Big Data",
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 computation, Bayesian modelling, Bayesian statistics, Scalable algorithms",
author = "Farzana Jahan and Insha Ullah and Mengersen, {Kerrie L.}",
note = "Publisher Copyright: {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2020",
doi = "10.1007/978-3-030-42553-1_2",
language = "English",
isbn = "978-3-030-42552-4",
series = "Lecture Notes in Mathematics",
publisher = "Springer",
pages = "17--44",
editor = "Mengersen, {Kerrie L.} and Pierre Pudlo and Robert, {Christian P.}",
booktitle = "Case Studies in Applied Bayesian Data Science",
}