| |
| |
| |
| Introduction | |
| |
| |
| |
| A Basic Paradigm for Marketing Problems | |
| |
| |
| |
| A Simple Example | |
| |
| |
| |
| Benefits and Costs of the Bayesian Approach | |
| |
| |
| |
| An Overview of Methodological Material and Case Studies | |
| |
| |
| |
| Computing and This Book | |
| |
| |
| Acknowledgements | |
| |
| |
| |
| Bayesian Essentials | |
| |
| |
| |
| Essential Concepts from Distribution Theory | |
| |
| |
| |
| The Goal of Inference and Bayes' Theorem | |
| |
| |
| |
| Conditioning and the Likelihood Principle | |
| |
| |
| |
| Prediction and Bayes | |
| |
| |
| |
| Summarizing the Posterior | |
| |
| |
| |
| Decision Theory, Risk, and the Sampling Properties of Bayes Estimators | |
| |
| |
| |
| Identification and Bayesian Inference | |
| |
| |
| |
| Conjugacy, Sufficiency, and Exponential Families | |
| |
| |
| |
| Regression and Multivariate Analysis Examples | |
| |
| |
| |
| Integration and Asymptotic Methods | |
| |
| |
| |
| Importance Sampling | |
| |
| |
| |
| Simulation Primer for Bayesian Problems | |
| |
| |
| |
| Simulation from Posterior of Multivariate Regression Model | |
| |
| |
| |
| Markov Chain Monte Carlo Methods | |
| |
| |
| |
| Markov Chain Monte Carlo Methods | |
| |
| |
| |
| A Simple Example: Bivariate Normal Gibbs Sampler | |
| |
| |
| |
| Some Markov Chain Theory | |
| |
| |
| |
| Gibbs Sampler | |
| |
| |
| |
| Gibbs Sampler for the Seemingly Unrelated Regression Model | |
| |
| |
| |
| Conditional Distributions and Directed Graphs | |
| |
| |
| |
| Hierarchical Linear Models | |
| |
| |
| |
| Data Augmentation and a Probit Example | |
| |
| |
| |
| Mixtures of Normals | |
| |
| |
| |
| Metropolis Algorithms | |
| |
| |
| |
| Metropolis Algorithms Illustrated with the Multinomial Logit Model | |
| |
| |
| |
| Hybrid Markov Chain Monte Carlo Methods | |
| |
| |
| |
| Diagnostics | |
| |
| |
| |
| Unit-Level Models and Discrete Demand | |
| |
| |
| |
| Latent Variable Models | |
| |
| |
| |
| Multinomial Probit Model | |
| |
| |
| |
| Multivariate Probit Model | |
| |
| |
| |
| Demand Theory and Models Involving Discrete Choice | |
| |
| |
| |
| Hierarchical Models for Heterogeneous Units | |
| |
| |
| |
| Heterogeneity and Priors | |
| |
| |
| |
| Hierarchical Models | |
| |
| |
| |
| Inference for Hierarchical Models | |
| |
| |
| |
| A Hierarchical Multinomial Logit Example | |
| |
| |
| |
| Using Mixtures of Normals | |
| |
| |
| |
| Further Elaborations of the Normal Model of Heterogeneity | |
| |
| |
| |
| Diagnostic Checks of the First-Stage Prior | |
| |
| |
| |
| Findings and Influence on Marketing Practice | |
| |
| |
| |
| Model Choice and Decision Theory | |
| |
| |
| |
| Model Selection | |
| |
| |
| |
| Bayes Factors in the Conjugate Setting | |
| |
| |
| |
| Asymptotic Methods for Computing Bayes Factors | |
| |
| |
| |
| Computing Bayes Factors Using Importance Sampling | |
| |
| |
| |
| Bayes Factors Using MCMC Draws | |
| |
| |
| |
| Bridge Sampling Methods | |
| |
| |
| |
| Posterior Model Probabilities with Unidentified Parameters | |
| |
| |
| |
| Chib's Method | |
| |
| |
| |
| An Example of Bayes Factor Computation: Diagonal Multinomial Probit Models | |
| |
| |
| |
| Marketing Decisions and Bayesian Decision Theory | |
| |
| |
| |
| An Example of Bayesian Decision Theory: Valuing Household Purchase Information | |
| |
| |
| |
| Simultaneity | |
| |
| |
| |
| A Bayesian Approach to Instrumental Variables | |
| |
| |
| |
| Structural Models and Endogeneity/Simultaneity | |
| |
| |
| |
| Nonrandom Marketing Mix Variables | |
| |
| |
| Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts | |
| |
| |
| Background | |
| |
| |
| Model | |
| |
| |
| Data | |
| |
| |
| Results | |
| |
| |
| Discussion | |
| |
| |
| R Implementation | |
| |
| |
| Case Study 2: Modeling Interdependent Consumer Preferences | |
| |
| |
| Background | |
| |
| |
| Model | |
| |
| |
| Data | |
| |
| |
| Results | |
| |
| |
| Discussion | |
| |
| |
| R Implementation | |
| |
| |
| Case Study 3: Overcoming Scale Usage Heterogeneity | |
| |
| |
| Background | |
| |
| |
| Model | |
| |
| |
| Priors and MCMC Algorithm | |
| |
| |
| Data | |
| |
| |
| Discussion | |
| |
| |
| R Implementation | |
| |
| |
| Case Study 4: A Choice Model with Conjunctive Screening Rules | |
| |
| |
| Background | |
| |
| |
| Model | |
| |
| |
| Data | |
| |
| |
| Results | |
| |
| |
| Discussion | |
| |
| |
| R Implementation | |
| |
| |
| Case Study 5: Modeling Consumer Demand for Variety | |
| |
| |
| Background | |
| |
| |
| Model | |
| |
| |
| Data | |
| |
| |
| Results | |
| |
| |
| Discussion | |
| |
| |
| R Implementation | |
| |
| |
| |
| An Introduction to Hierarchical Bayes Modeling in R | |
| |
| |
| |
| Setting Up the R Environment<$$$> | |