Bayesian Statistics and Marketing

Peter E. Rossi , Greg M. Allenby , Sanjog Misra 지음

Wiley 2024년 07월 08일 출간

작품소개

책소개

Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.

목차

1 Introduction 1 1.1 A Basic Paradigm for Marketing Problems 2 1.2 A Simple Example 3 1.3 Benefits and Costs of the Bayesian Approach 5 1.4 An Overview of Methodological Material and Case Studies 6 1.5 Approximate Bayes Methods and This Book 7 1.6 Computing and This Book 8 2 Bayesian Essentials 11 2.1 Essential Concepts from Distribution Theory 11 2.2 The Goal of Inference and Bayes Theorem 15 2.3 Conditioning and the Likelihood Principle 16 2.4 Prediction and Bayes 17 2.5 Summarizing the Posterior 17 2.6 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators 18 2.7 Identification and Bayesian Inference 20 2.8 Conjugacy, Sufficiency, and Exponential Families 21 2.9 Regression and Multivariate Analysis Examples 23 2.10 Integration and Asymptotic Methods 37 2.11 Importance Sampling 38 2.12 Simulation Primer for Bayesian Problems 42 2.13 Simulation from Posterior of Multivariate Regression Model 47 3 MCMC Methods 49 3.1 MCMC Methods 50 3.2 A Simple Example: Bivariate Normal Gibbs Sampler 52 3.3 Some Markov Chain Theory 57 3.4 Gibbs Sampler 63 3.5 Gibbs Sampler for the SUR Regression Model 64 3.6 Conditional Distributions and Directed Graphs 66 3.7 Hierarchical Linear Models 69 3.8 Data Augmentation and a Probit Example 74 3.9 Mixtures of Normals 78 3.10 Metropolis Algorithms 85 3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model 92 3.12 Hybrid MCMC Methods 95 3.13 Diagnostics 98 4 Unit-Level Models and Discrete Demand 103 4.1 Latent Variable Models 104 4.2 Multinomial Probit Model 106 4.3 Multivariate Probit Model 116 4.4 Demand Theory and Models Involving Discrete Choice 121 5 Hierarchical Models for Heterogeneous Units 129<

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작가정보

저자(글) Peter E. Rossi

저자(글) Greg M. Allenby

저자(글) Sanjog Misra