Bayesian Models

  • Thomas OtterEmail author
Living reference work entry


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.


Marketing decision-making Bayesian inference Gibbs sampling Metropolis Hastings Hamiltonian Monte Carlo R bayesm Stan 



I would like to thank Anocha Aribarg, Albert Bemmaor, Joachim Büschken, Arash Laghaie, anonymous reviewers, the editors, and participants in my class on “Bayesian Modeling for Marketing” helpful comments and feedback. All remaining errors are obviously mine.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Goethe University Frankfurt am MainFrankfurt am MainGermany

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