Bayesian Methods for Management and Business : Pragmatic Solutions for Real Problems.

Cover -- Title Page -- Copyright -- Dedication -- Preface -- Chapter 1: Introduction to Bayesian Methods -- 1.1 Bayesian Methods: An Aerial Survey -- 1.2 Bayes' Theorem -- 1.3 Bayes' Theorem and the Focus Group -- 1.4 The Flavors of Probability -- 1.5 Summary -- 1.6 Notation Introduced in this Chapter -- Chapter 2: A First Look at Bayesian Computation -- 2.1 Getting Started -- 2.2 Selecting the Likelihood Function -- 2.3 Selecting the Functional Form -- 2.4 Selecting the Prior -- 2.5 Finding the Normalizing Constant -- 2.6 Obtaining the Posterior -- 2.7 Communicating Findings -- 2.8 Predicting Future Outcomes -- 2.9 Summary -- 2.10 Exercises -- 2.11 Notation Introduced in this Chapter -- Chapter 3: Computer-Assisted Bayesian Computation -- 3.1 Getting Started -- 3.2 Random Number Sequences -- 3.3 Monte Carlo Integration -- 3.4 Monte Carlo Simulation for Inference -- 3.5 The Conjugate Normal Model -- 3.6 In Practice: Inference for the Conjugate Normal Model -- 3.7 Count Data and the Conjugate Poisson Model -- 3.8 Summary -- 3.9 Exercises -- 3.10 Notation Introduced in this Chapter -- 3.11 Appendix-In Detail: Finding Posterior Distributions for the Normal Model -- Chapter 4: Markov Chain Monte Carlo and Regression Models -- 4.1 Introduction to Markov Chain Monte Carlo -- 4.2 Fundamentals of MCMC -- 4.3 Gibbs Sampling -- 4.4 Gibbs Sampling and the Simple Linear Regression Model -- 4.5 In Practice: The Simple Linear Regression Model -- 4.6 The Metropolis Algorithm -- 4.7 Hastings' Extension of the Metropolis Algorithm -- 4.8 Summary -- 4.9 Exercises -- Chapter 5: Estimating Bayesian Models With WinBUGS -- 5.1 An Introduction to WinBUGS -- 5.2 In Practice: A First WinBUGS MODEL -- 5.3 In Practice: Models for the Mean in WinBUGS -- 5.4 Examining The Prior's Influence with Sensitivity Analysis -- 5.5 In Practice: Examining Proportions In WinBUGS.

5.6 Analysis of Variance Models -- 5.7 Higher Order ANOVA Models -- 5.8 Regression and ANCOVA Models in WinBUGS -- 5.9 Summary -- 5.10 Chapter Appendix: Exporting WinBUGS MCMC Output TO R -- 5.11 Exercises -- Chapter 6: Assessing Mcmc Performance in WinBUGS -- 6.1 Convergence Issues in MCMC Modeling -- 6.2 Output Diagnostics in WinBUGS -- 6.3 Reparameterizing to Improve Convergence -- 6.4 Number and Length of Chains -- 6.5 Metropolis-Hastings Acceptance Rates -- 6.6 Summary -- 6.7 Exercises -- Chapter 7: Model Checking and Model Comparison -- 7.1 Graphical Model Checking -- 7.2 Predictive Densities and Checking Model Assumptions -- 7.3 Variable Selection Methods -- 7.5 Deviance Information Criterion -- 7.6 Summary -- 7.7 Exercises -- Chapter 8: Hierarchical Models -- 8.1 Fundamentals of Hierarchical Models -- 8.2 The Random Coefficients Model -- 8.3 Hierarchical Models for Variance Terms -- 8.4 Functional Forms at Multiple Hierarchical Levels -- 8.5 In Detail: Modeling Covarying Hierarchical Terms -- 8.6 Summary -- 8.7 Exercises -- 8.8 Notation Introduced in this Chapter -- Chapter 9: Generalized Linear Models -- 9.1 Fundamentals of Generalized Linear Models -- 9.2 Count Data Models: Poisson Regression -- 9.3 Models for Binary Data: Logistic Regression -- 9.4 The Probit Model -- 9.5 In Detail: Multinomial Logistic Regression for Categorical Outcomes -- 9.6 Hierarchical Models for Count Data -- 9.7 Hierarchical Models for Binary Data -- 9.8 Summary -- 9.9 Exercises -- 9.10 Notation Introduced in this Chapter -- Chapter 10: Models For Difficult Data -- 10.1 Living with Outliers-Robust Regression Models -- 10.2 Handling Heteroscedasticity by Modeling Variance Parameters -- 10.3 Dealing with Missing Data -- 10.4 Types of Missing Data -- 10.5 Missing Covariate Data and Non-Normal Missing Data -- 10.6 Summary -- 10.7 Exercises.

10.8 Notation Introduced in this Chapter -- Chapter 11: Introduction To Latent Variable Models -- 11.1 Not Seen but Felt -- 11.2 Latent Variable Models for Binary Data -- 11.3 Structural Break Models -- 11.4 In Detail: The Ordinal Probit Model -- 11.5 Summary -- 11.6 Exercises -- Appendix A: Common Statistical Distributions -- References -- Author Index -- Subject Index -- End User License Agreement.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.