Bayesian Models

Bayesian models have become a mainstay in the tool set for marketing research in academia and industry practice. In this chapter, I discuss the advantages the Bayesian approach offers to researchers in marketing, the essential building blocks of a Bayesian model, Bayesian model comparison, and useful algorithmic approaches to fully Bayesian estimation. I show how to achieve feasible Bayesian inference to support marketing decisions under uncertainty using the Gibbs sampler, the Metropolis Hastings algorithm, and point to more recent developments – specifically the no-U-turn implementation of Hamiltonian Monte Carlo sampling available in Stan. The emphasis is on the development of an appreciation of Bayesian inference techniques supported by references to implementations in the open source software R, and not on the discussion of individual models. The goal is to encourage researchers to formulate new, more complete, and useful prior structures that can be updated with data for better marketing decision support.