## Bayesian Statistics and MarketingThe past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.
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.
Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike. |

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### Contents

1 | |

9 | |

3 Markov Chain Monte Carlo Methods | 49 |

4 UnitLevel Models and Discrete Demand | 103 |

5 Hierarchical Models for Heterogeneous Units | 129 |

6 Model Choice and Decision Theory | 159 |

7 Simultaneity | 185 |

Case Study 1 A Choice Model for Packaged Goods Dealing with Discrete Quantities and Quantity Discounts | 207 |

Case Study 3 Overcoming Scale Usage Heterogeneity | 237 |

Case Study 4 A Choice Model with Conjunctive Screening Rules | 253 |

Case Study 5 Modeling Consumer Demand for Variety | 269 |

Appendix A An Introduction to Hierarchical Bayes Modeling in R | 279 |

Appendix B A Guide to Installation and Use of bayesm | 323 |

335 | |

341 | |

Case Study 2 Modeling Interdependent Consumer Preferences | 225 |

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### Common terms and phrases

Allenby alternative analysis applications approach approximation asymptotic attribute autocorrelation Bayes estimator Bayes factor Bayesian Bayesian inference bayesm bivariate brand choice model coefficients component compute conditional distribution conjugate prior consider consumers correlation covariance matrix defined demand dependence detailing discrete elements equation error evaluated example Figure Gibbs sampler given hierarchical models household identified importance sampling improper prior independent inference integral intercepts inverted Wishart joint distribution large number latent variable likelihood likelihood function linear marginal density marginal distribution marginal utility Markov chain McCulloch MCMC MCMC draws MCMC methods mixture of normals MNP model model parameters multinomial multivariate normal natural conjugate prior normal distribution normal prior observed optimal package parameter space posterior distribution posterior means probability probit model problem purchase quantity random effects regression model respondents Rossi scale usage simulation specification standard Statistical structure unit unit-level univariate utility function vector