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Bayesian Statistics and Marketing
出版済み 3-5週間でお届けいたします。
Series: Wiley Series in Probability and Statistics Author: Rossi, Peter E. (University of Chicago, USA) / Allenby, Greg M. (Ohio State University, USA) / Misra, Sanjog (University of Chicago, USA) Publisher: WILEY ISBN: 9781394219117 Cover: HARDCOVER Date: 2024年08月 こちらの商品は学校・法人様向け（機関契約）のオンラインブック版がございます。 オンラインブックの価格、納期につきましては弊社営業員または当ECサイトよりお問い合わせください。  DESCRIPTION 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. TABLE OF CONTENTS 1 Introduction 2 Bayesian Essentials 3 MCMC Methods 4 Unit-Level Models and Discrete Demand 5 Hierarchical Models for Heterogeneous Units 6 Model Choice and Decision Theory 7 Simultaneity 8 A Bayesian Perspective on Machine Learning 9 Bayesian Analysis for Text Data 10 Case Study 1: Analysis of Choice-Based Conjoint Data Using A Hierarchical Logit Model 11 Case Study 2: WTP and Equilibrium Analysis with Conjoint Demand 12 Case Study 3: Scale Usage Heterogeneity 13 Case Study 4: Volumetric Conjoint 14 Case Study 5: Approximate Bayes and Personalized Pricing
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