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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.
Autorentext
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 journals that discuss Bayesian methods.
Klappentext
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:
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 journals that discuss Bayesian methods.
Inhalt
Preface xv
1 Introduction to Bayesian Methods 1
1.1 Bayesian Methods: An Aerial Survey 1
1.1.1 Informal Example 3
1.2 Bayes' Theorem 4
1.3 Bayes' Theorem and the Focus Group 6
1.4 The Flavors of Probability 8
1.4.1 Common Ground 9
1.4.2 Frequency-Based Probability 9
1.4.3 Subjective Probability 10
1.5 Summary 11
1.6 Notation Introduced in this Chapter 11
2 A First Look at Bayesian Computation 12
2.1 Getting Started 12
2.2 Selecting the Likelihood Function 13
2.3 Selecting the Functional Form 16
2.4 Selecting the Prior 17
2.5 Finding the Normalizing Constant 18
2.6 Obtaining the Posterior 19
2.7 Communicating Findings 23
2.8 Predicting Future Outcomes 26
2.9 Summary 28
2.10 Exercises 28
2.11 Notation Introduced in this Chapter 29
3 Computer-Assisted Bayesian Computation 30
3.1 Getting Started 30
3.2 Random Number Sequences 31
3.3 Monte Carlo Integration 33
3.4 Monte Carlo Simulation for Inference 36
3.4.1 Testing for a Difference in Proportions 37
3.4.2 Predicting Customer Behavior 38
3.4.3 Predicting Customer Behavior Part 2 40
3.5 The Conjugate Normal Model 40
3.5.1 The Conjugate Normal Model: Mean with Variance Known 40
3.5.2 The Conjugate Normal Model: Variance with Mean Known 42
3.5.3 The Conjugate Normal Model with Mean and Variance Both Unknown 44
3.6 In Practice: Inference for the Conjugate Normal Model 45
3.6.1 Conjugate Normal Mean with Variance Known 46
3.6.2 Conjugate Normal Variance with Mean Known 47
3.6.3 Conjugate Normal Mean and Variance Both Unknown 48
3.7 Count Data and the Conjugate Poisson Model 52
3.7.1 In Detail: Conjugate Poisson Model Development 53
3.7.2 In Practice: Inference for the Conjugate Poisson Model 54
3.8 Summary 56
3.9 Exercises 56
3.10 Notation Introduced in this Chapter 58
3.11 AppendixIn Detail: Finding Posterior Distributions for the Normal Model 58
3.11.1 Analysis of the Normal Mean with Variance Known 59
3.11.2 Analysis of the Normal Variance with Mean Known 61
3.11.3 Analysis of the Conjugate Normal Model with Mean and Variance Both Unknown 62
4 Markov Chain Monte Carlo and Regression Models 64
4.1 Introduction to Markov Chain Monte Carlo 64
4.2 Fundamentals of MCMC 66
4.3 Gibbs Sampling 67
4.3.1 Gibbs Sampling for the Normal Mean 69
4.3.2 Output Analysis 70
4.4 Gibbs Sampling and the Simple Linear Regression Model 73
4.5 In Practice: The Simple Linear Regression Model 76
4.6 The Metropolis Algorithm 79
4.6.1 In Practice: Simulating from a Standard Normal Distribution Using the Metropolis Algorithm 81
4.6.2 In Practice: Regression Analysis Using the Metropolis Algorithm 85
4.7 Hastings' Extension of the Metropolis Algorithm 87
4.7.1 In Practice: The MetropolisHastings Algorithm 89
4.7.2 The Relationship Between the Gibbs Sampler and the MetropolisHastings Algorithm 90
4.8 Summary 91
4.9 Exercises 92
5 Estimating Bayesian Models With WinBUGS 93
5.1 An Introduction to WinBUGS 94
5.2 In Practice: A First WinBUGS Model 95
5.3 In Practice: M…