Bayesian Statistics and Marketing

ISBN-10: 0470863676

ISBN-13: 9780470863671

Edition: 2005

List price: $95.99
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Description:

The past decade has seen an increase in the use of Bayesian methods in marketing due, in part, to computational & modelling breakthroughs, making its implementation ideal for many marketing problems. This text covers Bayesian methods in marketing, with common notation and algorithms for estimating the models.
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Book details

List price: $95.99
Copyright year: 2005
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/9/2005
Binding: Hardcover
Pages: 368
Size: 7.36" wide x 9.88" long x 1.05" tall
Weight: 2.068
Language: English

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<$$$>
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