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Bayes Linear Statistics, Theory and Methods - Michael Goldstein
book is out-of-stock
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Michael Goldstein:

Bayes Linear Statistics, Theory and Methods - new book

ISBN: 9780470065679

ID: 9780470065679

Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples.The book covers:The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.Simple ways to use partial prior specifications to adjust beliefs, given observations.Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.General approaches to statistical modelling based upon partial exchangeability judgements.Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book. Bayes Linear Statistics, Theory and Methods: Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples.The book covers:The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.Simple ways to use partial prior specifications to adjust beliefs, given observations.Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.General approaches to statistical modelling based upon partial exchangeability judgements.Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book. Bayessches Verfahren Bayes-Verfahren Statistik Bayesian Analysis Statistics, John Wiley & Sons

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Bayes Linear Statistics, Theory and Methods - Michael Goldstein
book is out-of-stock
(*)

Michael Goldstein:

Bayes Linear Statistics, Theory and Methods - new book

ISBN: 9780470065679

ID: 9780470065679

Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples.The book covers:The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.Simple ways to use partial prior specifications to adjust beliefs, given observations.Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.General approaches to statistical modelling based upon partial exchangeability judgements.Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book. Bayes Linear Statistics, Theory and Methods: Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples.The book covers:The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.Simple ways to use partial prior specifications to adjust beliefs, given observations.Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.General approaches to statistical modelling based upon partial exchangeability judgements.Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book. Bayes-Verfahren Bayesian Analysis Bayessches Verfahren Statistics Statistik, John Wiley & Sons

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Bayes Linear Statistics, Theory & Methods (eBook, PDF) - Goldstein, Michael; Wooff, David
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Goldstein, Michael; Wooff, David:
Bayes Linear Statistics, Theory & Methods (eBook, PDF) - new book

ISBN: 9780470065679

ID: f711cc160eb3e0888d5acf78dd161874

Bayesian methods combine information available from data with anyprior information available from expert knowledge. The Bayes linearapproach follows this path, offering a quantitative structure forexpressing beliefs, and systematic methods for adjusting thesebeliefs, given observational data. The methodology differs from thefull Bayesian methodology in that it establishes simpler approachesto belief specification and analysis based around expectationjudgements. Bayes Linear Statistics presents anauthoritative account of this approach, explaining the foundations,theory, methodology, and practicalities of this important field.The text provides a thorough coverage of Bayes linear analysis,from the development of the basic language to the collection ofalgebraic results needed for efficient implementation, withdetailed practical examples.The book covers:* The importance of partial prior specifications for complexproblems where it is difficult to supply a meaningful full priorprobability specification.* Simple ways to use partial prior specifications to adjustbeliefs, given observations.* Interpretative and diagnostic tools to display the implicationsof collections of belief statements, and to make stringentcomparisons between expected and actual observations.* General approaches to statistical modelling based upon partialexchangeability judgements.* Bayes linear graphical models to represent and display partialbelief specifications, organize computations, and display theresults of analyses.Bayes Linear Statistics is essential reading for allstatisticians concerned with the theory and practice of Bayesianmethods. There is an accompanying website hosting free software andguides to the calculations within the book. E-Book, [PU: Wiley]

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Bayes Linear Statistics, Theory and Methods - Michael Goldstein
book is out-of-stock
(*)
Michael Goldstein:
Bayes Linear Statistics, Theory and Methods - new book

ISBN: 9780470065679

ID: 9780470065679

Bayesian methods combine information available from data with anyprior information available from expert knowledge. The Bayes linearapproach follows this path, offering a quantitative structure forexpressing beliefs, and systematic methods for adjusting thesebeliefs, given observational data. The methodology differs from thefull Bayesian methodology in that it establishes simpler approachesto belief specification and analysis based around expectationjudgements. Bayes Linear Statistics presents anauthoritative account of this approach, explaining the foundations,theory, methodology, and practicalities of this important field. Bayes Linear Statistics, Theory and Methods: Bayesian methods combine information available from data with anyprior information available from expert knowledge. The Bayes linearapproach follows this path, offering a quantitative structure forexpressing beliefs, and systematic methods for adjusting thesebeliefs, given observational data. The methodology differs from thefull Bayesian methodology in that it establishes simpler approachesto belief specification and analysis based around expectationjudgements. Bayes Linear Statistics presents anauthoritative account of this approach, explaining the foundations,theory, methodology, and practicalities of this important field. Bayessches Verfahren Statistics Statistik Bayesian Analysis Bayes-Verfahren, John Wiley & Sons

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Bayes Linear Statistics, Theory & Methods - Michael Goldstein;  David Wooff
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Michael Goldstein; David Wooff:
Bayes Linear Statistics, Theory & Methods - First edition

2007, ISBN: 9780470065679

ID: 25452690

[ED: 1], Auflage, eBook Download (PDF), eBooks, [PU: Wiley]

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Details of the book
Bayes Linear Statistics, Theory   Methods
Author:

Goldstein, Michael;Wooff, David

Title:

Bayes Linear Statistics, Theory Methods

ISBN:

0470065672

Details of the book - Bayes Linear Statistics, Theory Methods


EAN (ISBN-13): 9780470065679
ISBN (ISBN-10): 0470065672
Publishing year: 2007
Publisher: Wiley, J
536 Pages
Language: eng/Englisch

Book in our database since 05.02.2010 01:50:25
Book found last time on 11.09.2016 08:57:01
ISBN/EAN: 0470065672

ISBN - alternate spelling:
0-470-06567-2, 978-0-470-06567-9

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