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
Chapter 1. Introduction To Bayesian Methods
Chapter 2. A First Look At Bayesian Computation
Chapter 3. Computer-Assisted Bayesian Computation
Chapter 4. Markov Chain Monte Carlo And Regression Models
Chapter 5. Estimating Bayesian Models With Winbugs
Chapter 6. Assessing Mcmc Performance In Winbugs
Chapter 7. Model Checking And Model Comparison
Chapter 8. Hierarchical Models
Chapter 9. Generalized Linear Models
Chapter 10. Models For Difficult Data
Chapter 11. Introduction To Latent Variable Models
Appendix A: Common Statistical Distributions