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
来源：
Customer Needs and Solutions | 2014年 / 1卷 / 3期
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
相关论文
共 37 条
  • [1] Allenby GM(1999)Marketing models of consumer heterogeneity J Econ 89 57-78
  • [2] Rossi PE(2002)Bayesian inference for the negative binomial distribution via polynomial expansions J Comput Graph Stat 11 189-201
  • [3] Bradlow ET(2011)Identifying unmet demand Mark Sci 30 61-73
  • [4] Hardie BGS(2010)BART: Bayesian additive regression trees Ann Appl Stat 4 266-298
  • [5] Fader PS(2002)Bayesian inference for the beta-binomial distribution via polynomial expansions J Comput Graph Stat 11 202-207
  • [6] Chankukala S(2014)Conceptualizing and measuring prospect wants: understanding the source of brand preference Cust Needs Solutions 1 23-39
  • [7] Edwards Y(1990)Sampling-based approaches to calculating marginal densities J Am Stat Assoc 85 398-409
  • [8] Allenby GM(1993)Variable selection via Gibbs sampling J Am Stat Assoc 88 881-889
  • [9] Chipman H(1999)An introduction to variational methods for graphical models Mach Learn 37 183-233
  • [10] George E(2002)Modeling consumer demand for variety Mark Sci 21 229-250
1234