ISBN10: 0470015624, ISBN13: 9780470015629, [publisher: John Wiley & Sons] Hardcover Michael Goldstein, Professor of Statistics, Department of Mathematical Sciences, University of DurhamMichael Goldstein has worked on and researched the Bayes linear approach for around 30 years, his general interests being in the foundations, methodology an.
[Greven, Germany] [Publication Year: 2007]
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ISBN10: 0470015624, ISBN13: 9780470015629, [publisher: John Wiley & Sons Inc, New York] Hardcover First Edition Hardcover. 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 ...

ISBN10: 0470015624, ISBN13: 9780470015629, [publisher: John Wiley & Sons Inc, New York] Hardcover First Edition Hardcover. 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 ...