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9781118637555 English 1118637550 Features 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 "alsofeatures: 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 provide insight into important issues facing business and managementThe use of WinBUGS and R to showcase the benefits of Bayesian statistics for the increasingly data-rich business environment"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 need to broaden their methodological skill sets., 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 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., This book begins by motivating the use of Bayesian statistics as a natural way of revising beliefs with empirical data. Basic computational issues are discussed and then computer-assisted methods for Bayesian computation are covered. The linear model, which continues to have many applications in the business disciplines, is addressed, and the importance of sensitivity analysis and monitoring MCMC performance is emphasized. In addition, model comparison is discussed since it is fundamental to the business disciplines. More advanced models including hierarchical models, generalized linear models, and latent variable models are presented, providing readers with experience using these more advanced models. Throughout, emphasis is placed on practical applications with frequent forays into " In Practice " book sections. In these sections, a worked example is provided using business data sets drawn from multiple disciplines and associated. WinBUGS and R code is included for these examples and is discussed in parallel to the example. The idea of these sections is to embed the practical orientation of Bayesian statistics using these freely available tools. Each chapter concludes with an exercise section and summary. Chapter coverage includes: Introduction to Bayesian Methods (introduces Bayesian key concepts for usage throughout the book); A First Look at Bayesian Computation (provides an overview of analytic computation and distributional considerations for inference and discusses binomial data and the beta distribution); Computer-Assisted Bayesian Computation (introduces the power of Monte Carlo computational techniques in the context of Bayesian inference, described conjugate analysis in detail, and discusses inference for the normal and Poisson distributions); M arkov Chain Monte Carlo and Regression Models (illustrates Markov chain Monte Carlo computational techniques in the context of Bayesian inference and discusses the simple linear regression model); Regression Models Using WinBUGS (illustrates that WinBUGS software can be used to undertake Gibbs and Metropolis sampling, which is advantageous for managers since more time can be spent on the modeling and the examination of results as opposed to customized writing of MCMC samplers); Assessing MCMC Performance (discusses that it is critically important to ensure that the Markov chain is simulating from the posterior and provided tools for examining this issue); Model Checking and Model Comparison (examines methods for model comparison and contrasts the characteristics of the different methods); Hierarchal Models (illustrates hierarchical models from a Bayesian approach using WinBUGS and describes that these models have much to offer those wishing to understand business problems and are a natural extension of conventional linear models); Generalized Linear Models (illustrates generalized linear models from a Bayesian approach using WinBUGS and addresses that often times business data does not take the form of continuous data so generalized linear models add much value to business insight); Models for Difficult Data (addresses ways to analyze data that do not conform to standard assumptions); and Introduction to Latent Variable Models (discusses Bayesian approaches to latent-data models since many important sources of business data cannot be directly observed).
9781118637555 English 1118637550 Features 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 "alsofeatures: 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 provide insight into important issues facing business and managementThe use of WinBUGS and R to showcase the benefits of Bayesian statistics for the increasingly data-rich business environment"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 need to broaden their methodological skill sets., 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 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., This book begins by motivating the use of Bayesian statistics as a natural way of revising beliefs with empirical data. Basic computational issues are discussed and then computer-assisted methods for Bayesian computation are covered. The linear model, which continues to have many applications in the business disciplines, is addressed, and the importance of sensitivity analysis and monitoring MCMC performance is emphasized. In addition, model comparison is discussed since it is fundamental to the business disciplines. More advanced models including hierarchical models, generalized linear models, and latent variable models are presented, providing readers with experience using these more advanced models. Throughout, emphasis is placed on practical applications with frequent forays into " In Practice " book sections. In these sections, a worked example is provided using business data sets drawn from multiple disciplines and associated. WinBUGS and R code is included for these examples and is discussed in parallel to the example. The idea of these sections is to embed the practical orientation of Bayesian statistics using these freely available tools. Each chapter concludes with an exercise section and summary. Chapter coverage includes: Introduction to Bayesian Methods (introduces Bayesian key concepts for usage throughout the book); A First Look at Bayesian Computation (provides an overview of analytic computation and distributional considerations for inference and discusses binomial data and the beta distribution); Computer-Assisted Bayesian Computation (introduces the power of Monte Carlo computational techniques in the context of Bayesian inference, described conjugate analysis in detail, and discusses inference for the normal and Poisson distributions); M arkov Chain Monte Carlo and Regression Models (illustrates Markov chain Monte Carlo computational techniques in the context of Bayesian inference and discusses the simple linear regression model); Regression Models Using WinBUGS (illustrates that WinBUGS software can be used to undertake Gibbs and Metropolis sampling, which is advantageous for managers since more time can be spent on the modeling and the examination of results as opposed to customized writing of MCMC samplers); Assessing MCMC Performance (discusses that it is critically important to ensure that the Markov chain is simulating from the posterior and provided tools for examining this issue); Model Checking and Model Comparison (examines methods for model comparison and contrasts the characteristics of the different methods); Hierarchal Models (illustrates hierarchical models from a Bayesian approach using WinBUGS and describes that these models have much to offer those wishing to understand business problems and are a natural extension of conventional linear models); Generalized Linear Models (illustrates generalized linear models from a Bayesian approach using WinBUGS and addresses that often times business data does not take the form of continuous data so generalized linear models add much value to business insight); Models for Difficult Data (addresses ways to analyze data that do not conform to standard assumptions); and Introduction to Latent Variable Models (discusses Bayesian approaches to latent-data models since many important sources of business data cannot be directly observed).