Bayesian Statistics and Marketing

Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.

Written by the leading experts in the field, this unique book:

• Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.
• Provides a self-contained introduction to Bayesian methods.
• Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.
• Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies.

Peter E. Rossi is James Collins Professor of Marketing, Statistics and Economics at UCLA Anderson School of Management. Greg M. Allenby is the author of Bayesian Statistics and Marketing, published by Wiley.