Bayesian Methods for Management and Business

Eugene D. Hahn 지음

Wiley 2014년 08월 12일 출간

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HIGHLIGHTS THE USE OF BAYESIAN STATISTICS TO GAIN INSIGHTS FROM EMPIRICAL DATAFeaturing 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 systemsAn incremental skill-building presentation based on analyzing data sets with widely applicable models of increasing complexityAn accessible treatment of Bayesian statistics that is integrated with a broad range of business and management issues and problemsA practical problem-solving approach to illustrate how Bayesian statistics can help to provide insight into important issues facing business and managementBayesian 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 busines

목차

1 Introduction to Bayesian Methods 11.1 Bayesian Methods: An Aerial Survey 11.2 Bayes&' Theorem 41.3 Bayes&' Theorem and the Focus Group 61.4 The Flavors of Probability 91.5 Summary 121.6 Notation Introduced in This Chapter 122 A First Look at Bayesian Computation 132.1 Getting Started 132.2 Selecting the Likelihood Function 142.3 Selecting the Functional Form 182.4 Selecting the Prior 192.5 Finding the Normalizing Constant 202.6 Obtaining the Posterior 202.7 Communicating Findings 252.8 Predicting Future Outcomes 282.9 Summary 302.10 Exercises 312.11 Notation Introduced in This Chapter 323 Computer-Assisted Bayesian Computation 333.1 Getting Started 333.2 Random Number Sequences 343.3 Monte Carlo Integration 363.4 Monte Carlo Simulation for Inference 403.5 The Conjugate Normal Model 443.6 In Practice: The Conjugate Normal Model 503.7 Count Data and the Conjugate Poisson Model 573.8 Summary 613.9 Exercises 623.10 Notation Introduced in This Chapter 633.11 Appendix &n- In Detail: Finding Posterior Distributions for the Normal Model 634 MCMC and Regression Models 714.1 Introduction to Markov Chain Monte Carlo 714.2 Fundamentals of MCMC 734.3 Gibbs Sampling 754.4 Gibbs Sampling and the Simple Linear Regression Model 824.5 In Practice: The Simple Linear Regression Model 854.6 The Metropolis Algorithm 884.7 Hastings&' Extension of the Metropolis Algorithm 974.8 Summary 1024.9 Exercises 1035 Estimating Bayesian Models with WinBUGS 105

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저자(글) Eugene D. Hahn

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Eugene D. Hahn, PhD,is Associate Professor in the Department of Information and Decision Systems in the Franklin P. Perdue School of Business at Salisbury University. He has published in leading business and management journals as well as in statistical journals that discuss Bayesian methods.

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