Perspectives on Bayesian Methods and Big Data

被引:0
|
作者
Greg M. Allenby
Eric T. Bradlow
Edward I. George
John Liechty
Robert E. McCulloch
机构
[1] Fisher College of Business,The Wharton School
[2] The Ohio State University,Smeal College of Business
[3] University of Pennsylvania,Booth School of Business
[4] The Pennsylvania State University,undefined
[5] University of Chicago,undefined
关键词
Bayesian statistics; Big Data; Scalable computation;
D O I
10.1007/s40547-014-0017-9
中图分类号
学科分类号
摘要
Researchers and practitioners are facing a world with ever-increasing amounts of data and analytic tools, such as Bayesian inference algorithms, must be improved to keep pace with technology. Bayesian methods have brought substantial benefits to the discipline of Marketing Analytics, but there are inherent computational challenges with scaling them to Big Data. Several strategies with specific examples using additive regression trees and variable selection are discussed. In addition, the important observation is made that there are limits to the type of questions that can be answered using most of the Big Data available today.
引用
收藏
页码:169 / 175
页数:6
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