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Goldstein, Michael; Wooff, David - Bayes Linear Statistics - 9780470015629 - V9780470015629
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Bayes Linear Statistics

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Description for Bayes Linear Statistics 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. Series: Wiley Series in Probability and Statistics. Num Pages: 536 pages, illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 234 x 160 x 33. Weight in Grams: 880.
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...
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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.

Product Details

Publication date
2007
Publisher
John Wiley and Sons Ltd United States
Number of pages
536
Condition
New
Series
Wiley Series in Probability and Statistics
Number of Pages
544
Format
Hardback
Place of Publication
New York, United States
ISBN
9780470015629
SKU
V9780470015629
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

About Goldstein, Michael; Wooff, David
Michael Goldstein, Professor of Statistics, Department of Mathematical Sciences, University of Durham Michael Goldstein has worked on and researched the Bayes linear approach for around 30 years, his general interests being in the foundations, methodology and applications of Bayesian/subjectivist approaches to statistics. He has an outstanding reputation as one of the most original thinkers in the field, and was a...
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Michael Goldstein, Professor of Statistics, Department of Mathematical Sciences, University of Durham Michael Goldstein has worked on and researched the Bayes linear approach for around 30 years, his general interests being in the foundations, methodology and applications of Bayesian/subjectivist approaches to statistics. He has an outstanding reputation as one of the most original thinkers in the field, and was a contributing author to Wiley’s “Encyclopedia of Statistical Sciences”. David Wooff, Director of Statistics & Mathematics Consultancy Unit and Senior Lecturer in Statistics, Department of Mathematical Sciences, University of Durham David Wooff has been involved in a long collaboration for over 20 years with Michael Goldstein and others on developing Bayes linear methods, his primary research interest being the general development and application of Bayes linear methodology.

Reviews for Bayes Linear Statistics
“The book is an 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.” (Zentralblatt MATH, 2012) "Summarizing, the book is an interesting compendium of methods of updating beliefs." (Stat Papers, 2010) "The authors are to be congratulated for their...
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“The book is an 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.”  (Zentralblatt MATH, 2012) "Summarizing, the book is an interesting compendium of methods of updating beliefs." (Stat Papers, 2010) "The authors are to be congratulated for their pioneering effort in writing this book. Hopefully, more books and articles will follow, and the methodology will someday be part of mainstream statistics." (Technometrics, November 2008) "The authors are to be congratulated for their pioneering effort in writing this book.  Hopefully, more books and articles will follow, and the methodology will someday be part of mainstream statistics." (Technometrics, November 2008) "The book provides an extensive introduction and explanation of the subject and augments theory with numerous illustrative examples, including relevant considerations for specifying beliefs and diagnostics for assessing appropriateness." (Journal of the American Statistical Association, September 2008)

Goodreads reviews for Bayes Linear Statistics