Author: Thomas R. Palfrey
Publisher: CRC Press
ISBN: 1000111555
Category : Business & Economics
Languages : en
Pages : 122
Book Description
The implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a rigorous way the relationships between outcomes in a society and how those outcomes arise. The first part of "Bayesian Implementation" presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation theory. Substantive problems of interest such as public goods provision, auctions and bargaining are special cases of the model, and these are addressed in subsequent chapters.
Bayesian Implementation
Author: Thomas R. Palfrey
Publisher: CRC Press
ISBN: 1000111555
Category : Business & Economics
Languages : en
Pages : 122
Book Description
The implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a rigorous way the relationships between outcomes in a society and how those outcomes arise. The first part of "Bayesian Implementation" presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation theory. Substantive problems of interest such as public goods provision, auctions and bargaining are special cases of the model, and these are addressed in subsequent chapters.
Publisher: CRC Press
ISBN: 1000111555
Category : Business & Economics
Languages : en
Pages : 122
Book Description
The implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a rigorous way the relationships between outcomes in a society and how those outcomes arise. The first part of "Bayesian Implementation" presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation theory. Substantive problems of interest such as public goods provision, auctions and bargaining are special cases of the model, and these are addressed in subsequent chapters.
Bayesian Implementation
Author: Thomas R. Palfrey
Publisher: CRC Press
ISBN: 1000154645
Category : Business & Economics
Languages : en
Pages : 126
Book Description
The implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a rigorous way the relationships between outcomes in a society and how those outcomes arise. The first part of "Bayesian Implementation" presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation theory. Substantive problems of interest such as public goods provision, auctions and bargaining are special cases of the model, and these are addressed in subsequent chapters.
Publisher: CRC Press
ISBN: 1000154645
Category : Business & Economics
Languages : en
Pages : 126
Book Description
The implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a rigorous way the relationships between outcomes in a society and how those outcomes arise. The first part of "Bayesian Implementation" presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation theory. Substantive problems of interest such as public goods provision, auctions and bargaining are special cases of the model, and these are addressed in subsequent chapters.
Advancements in Bayesian Methods and Implementations
Author:
Publisher: Academic Press
ISBN: 0323952690
Category : Mathematics
Languages : en
Pages : 322
Book Description
Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Updated release includes the latest information on Advancements in Bayesian Methods and Implementation
Publisher: Academic Press
ISBN: 0323952690
Category : Mathematics
Languages : en
Pages : 322
Book Description
Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Updated release includes the latest information on Advancements in Bayesian Methods and Implementation
Bayesian Implementation
Author: Thomas R. Palfrey
Publisher:
ISBN: 9780415269070
Category : Bayesian statistical decision theory
Languages : en
Pages : 104
Book Description
Publisher:
ISBN: 9780415269070
Category : Bayesian statistical decision theory
Languages : en
Pages : 104
Book Description
Bayesian Modeling Using WinBUGS
Author: Ioannis Ntzoufras
Publisher: John Wiley & Sons
ISBN: 1118210352
Category : Mathematics
Languages : en
Pages : 477
Book Description
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.
Publisher: John Wiley & Sons
ISBN: 1118210352
Category : Mathematics
Languages : en
Pages : 477
Book Description
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.
The Bayesian Choice
Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 0387715983
Category : Mathematics
Languages : en
Pages : 620
Book Description
This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.
Publisher: Springer Science & Business Media
ISBN: 0387715983
Category : Mathematics
Languages : en
Pages : 620
Book Description
This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.
Bayesian Learning for Neural Networks
Author: Radford M. Neal
Publisher: Springer Science & Business Media
ISBN: 1461207452
Category : Mathematics
Languages : en
Pages : 194
Book Description
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Publisher: Springer Science & Business Media
ISBN: 1461207452
Category : Mathematics
Languages : en
Pages : 194
Book Description
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Bayesian Implementation
Author: John R. Duggan
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 266
Book Description
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 266
Book Description
Bayesian Implementation in Transferable Utility Environments, and Other Essays in Microeconomic Theory
Bayesian Structural Equation Modeling
Author: Sarah Depaoli
Publisher: Guilford Publications
ISBN: 1462547796
Category : Social Science
Languages : en
Pages : 550
Book Description
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.
Publisher: Guilford Publications
ISBN: 1462547796
Category : Social Science
Languages : en
Pages : 550
Book Description
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.