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

Book Description

HIGHLIGHTS THE USE OF BAYESIAN STATISTICS TO GAIN INSIGHTS FROM EMPIRICAL DATA

Featuring an accessible approach, Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems demonstrates how Bayesian statistics can help to provide insights into important issues facing business and management. The book draws on multidisciplinary applications and examples and utilizes the freely available software WinBUGS and R to illustrate the integration of Bayesian statistics within data-rich environments.

Computational issues are discussed and integrated with coverage of linear models, sensitivity analysis, Markov Chain Monte Carlo (MCMC), and model comparison. In addition, more advanced models including hierarchal models, generalized linear models, and latent variable models are presented to further bridge the theory and application in real-world usage.

Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems also features:

  • Numerous real-world examples drawn from multiple management disciplines such as strategy, international business, accounting, and information systems

  • An incremental skill-building presentation based on analyzing data sets with widely applicable models of increasing complexity

  • An accessible treatment of Bayesian statistics that is integrated with a broad range of business and management issues and problems

  • A practical problem-solving approach to illustrate how Bayesian statistics can help to provide insight into important issues facing business and management

  • Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems is an important textbook for Bayesian statistics courses at the advanced MBA-level and also for business and management PhD candidates as a first course in methodology. In addition, the book is a useful resource for management scholars and practitioners as well as business academics and practitioners who seek to broaden their methodological skill sets.

    Table of Contents

    1. Cover
    2. Title Page
    3. Copyright
    4. Dedication
    5. Preface
    6. Chapter 1: Introduction to Bayesian Methods
      1. 1.1 Bayesian Methods: An Aerial Survey
      2. 1.2 Bayes’ Theorem
      3. 1.3 Bayes’ Theorem and the Focus Group
      4. 1.4 The Flavors of Probability
      5. 1.5 Summary
      6. 1.6 Notation Introduced in this Chapter
    7. Chapter 2: A First Look at Bayesian Computation
      1. 2.1 Getting Started
      2. 2.2 Selecting the Likelihood Function
      3. 2.3 Selecting the Functional Form
      4. 2.4 Selecting the Prior
      5. 2.5 Finding the Normalizing Constant
      6. 2.6 Obtaining the Posterior
      7. 2.7 Communicating Findings
      8. 2.8 Predicting Future Outcomes
      9. 2.9 Summary
      10. 2.10 Exercises
      11. 2.11 Notation Introduced in this Chapter
    8. Chapter 3: Computer-Assisted Bayesian Computation
      1. 3.1 Getting Started
      2. 3.2 Random Number Sequences
      3. 3.3 Monte Carlo Integration
      4. 3.4 Monte Carlo Simulation for Inference
      5. 3.5 The Conjugate Normal Model
      6. 3.6 In Practice: Inference for the Conjugate Normal Model
      7. 3.7 Count Data and the Conjugate Poisson Model
      8. 3.8 Summary
      9. 3.9 Exercises
      10. 3.10 Notation Introduced in this Chapter
      11. 3.11 Appendix—In Detail: Finding Posterior Distributions for the Normal Model
    9. Chapter 4: Markov Chain Monte Carlo and Regression Models
      1. 4.1 Introduction to Markov Chain Monte Carlo
      2. 4.2 Fundamentals of MCMC
      3. 4.3 Gibbs Sampling
      4. 4.4 Gibbs Sampling and the Simple Linear Regression Model
      5. 4.5 In Practice: The Simple Linear Regression Model
      6. 4.6 The Metropolis Algorithm
      7. 4.7 Hastings’ Extension of the Metropolis Algorithm
      8. 4.8 Summary
      9. 4.9 Exercises
    10. Chapter 5: Estimating Bayesian Models With WinBUGS
      1. 5.1 An Introduction to <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i>
      2. 5.2 In Practice: A First <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i> MODEL MODEL
      3. 5.3 In Practice: Models for the Mean in <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i>
      4. 5.4 Examining The Prior's Influence with Sensitivity Analysis
      5. 5.5 In Practice: Examining Proportions In <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i>
      6. 5.6 Analysis of Variance Models
      7. 5.7 Higher Order ANOVA Models
      8. 5.8 Regression and ANCOVA Models in <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i>
      9. 5.9 Summary
      10. 5.10 Chapter Appendix: Exporting <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i> MCMC Output TO MCMC Output TO <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">R</i>
      11. 5.11 Exercises
    11. Chapter 6: Assessing Mcmc Performance in WinBUGS
      1. 6.1 Convergence Issues in MCMC Modeling
      2. 6.2 Output Diagnostics in <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">WinBUGS</i>
      3. 6.3 Reparameterizing to Improve Convergence
      4. 6.4 Number and Length of Chains
      5. 6.5 Metropolis–Hastings Acceptance Rates
      6. 6.6 Summary
      7. 6.7 Exercises
    12. Chapter 7: Model Checking and Model Comparison
      1. 7.1 Graphical Model Checking
      2. 7.2 Predictive Densities and Checking Model Assumptions
      3. 7.3 Variable Selection Methods
      4. 7.5 Deviance Information Criterion
      5. 7.6 Summary
      6. 7.7 Exercises
    13. Chapter 8: Hierarchical Models
      1. 8.1 Fundamentals of Hierarchical Models
      2. 8.2 The Random Coefficients Model
      3. 8.3 Hierarchical Models for Variance Terms
      4. 8.4 Functional Forms at Multiple Hierarchical Levels
      5. 8.5 In Detail: Modeling Covarying Hierarchical Terms
      6. 8.6 Summary
      7. 8.7 Exercises
      8. 8.8 Notation Introduced in this Chapter
    14. Chapter 9: Generalized Linear Models
      1. 9.1 Fundamentals of Generalized Linear Models
      2. 9.2 Count Data Models: Poisson Regression
      3. 9.3 Models for Binary Data: Logistic Regression
      4. 9.4 The Probit Model
      5. 9.5 In Detail: Multinomial Logistic Regression for Categorical Outcomes
      6. 9.6 Hierarchical Models for Count Data
      7. 9.7 Hierarchical Models for Binary Data
      8. 9.8 Summary
      9. 9.9 Exercises
      10. 9.10 Notation Introduced in this Chapter
    15. Chapter 10: Models For Difficult Data
      1. 10.1 Living with Outliers—Robust Regression Models
      2. 10.2 Handling Heteroscedasticity by Modeling Variance Parameters
      3. 10.3 Dealing with Missing Data
      4. 10.4 Types of Missing Data
      5. 10.5 Missing Covariate Data and Non-Normal Missing Data
      6. 10.6 Summary
      7. 10.7 Exercises
      8. 10.8 Notation Introduced in this Chapter
    16. Chapter 11: Introduction To Latent Variable Models
      1. 11.1 Not Seen but Felt
      2. 11.2 Latent Variable Models for Binary Data
      3. 11.3 Structural Break Models
      4. 11.4 In Detail: The Ordinal Probit Model
      5. 11.5 Summary
      6. 11.6 Exercises
    17. Appendix A: Common Statistical Distributions
    18. References
    19. Author Index
    20. Subject Index
    21. End User License Agreement