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
- Cover
- Title Page
- Copyright
- Dedication
- Preface
- Chapter 1: Introduction to Bayesian Methods
-
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
- Chapter 7: Model Checking and Model Comparison
- Chapter 8: Hierarchical Models
-
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
- Chapter 11: Introduction To Latent Variable Models
- Appendix A: Common Statistical Distributions
- References
- Author Index
- Subject Index
- End User License Agreement
Product information
- Title: Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems
- Author(s):
- Release date: September 2014
- Publisher(s): Wiley
- ISBN: 9781118637555
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