Perspectives on Bayesian Methods and Big Data

Greg M. Allenby, Eric T. Bradlow, Edward I. George, John Liechty, Robert E. McCulloch

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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 sub-stantial 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.

Original languageEnglish (US)
Pages (from-to)169-175
Number of pages7
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number3
StatePublished - Sep 2014
Externally publishedYes


  • Bayesian statistics
  • Big Data
  • Scalable computation

ASJC Scopus subject areas

  • Statistics and Probability
  • General Environmental Science
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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