A Survey of Bayesian Statistical Approaches for Big Data

被引:4
作者
Jahan, Farzana [1 ]
Ullah, Insha [1 ]
Mengersen, Kerrie L. [1 ]
机构
[1] Queensland Univ Technol, ARC Ctr Math & Stat Frontiers, Fac Sci & Engn, Sch Math Sci, Brisbane, Qld, Australia
来源
CASE STUDIES IN APPLIED BAYESIAN DATA SCIENCE: CIRM JEAN-MORLET CHAIR, FALL 2018 | 2020年 / 2259卷
基金
澳大利亚研究理事会;
关键词
Bayesian statistics; Bayesian modelling; Bayesian computation; Scalable algorithms; DATA ANALYTICS; MODEL SELECTION; HEALTH-CARE; INFERENCE; COMPUTATION; CHALLENGES; FUTURE; TECHNOLOGIES; OPPORTUNITIES; SIMULATION;
D O I
10.1007/978-3-030-42553-1_2
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页码:17 / 44
页数:28
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