{"product_id":"bayes-linear-statistics-isbn-9780470015629","title":"Bayes Linear Statistics","description":"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. \u003ci\u003eBayes Linear Statistics\u003c\/i\u003e presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.  \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eThe book covers:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eThe importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.\u003c\/li\u003e \u003cli\u003eSimple ways to use partial prior specifications to adjust beliefs, given observations.\u003c\/li\u003e \u003cli\u003eInterpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.\u003c\/li\u003e \u003cli\u003eGeneral approaches to statistical modelling based upon partial exchangeability judgements.\u003c\/li\u003e \u003cli\u003eBayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBayes Linear Statistics\u003c\/i\u003e 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.\u003c\/p\u003e  Preface.  \u003cp\u003e1 The Bayes linear approach.\u003c\/p\u003e \u003cp\u003e2 Expectation.\u003c\/p\u003e \u003cp\u003e3 Adjusting beliefs.\u003c\/p\u003e \u003cp\u003e4 The observed adjustment.\u003c\/p\u003e \u003cp\u003e5 Partial Bayes linear analysis.\u003c\/p\u003e \u003cp\u003e6 Exchangeable beliefs.\u003c\/p\u003e \u003cp\u003e7 Co-exchangeable beliefs.\u003c\/p\u003e \u003cp\u003e8 Learning about population variances.\u003c\/p\u003e \u003cp\u003e9 Belief comparison.\u003c\/p\u003e \u003cp\u003e10 Bayes linear graphical models.\u003c\/p\u003e \u003cp\u003e11 Matrix algebra for implementing the theory.\u003c\/p\u003e \u003cp\u003e12 Implementing Bayes linear statistics.\u003c\/p\u003e \u003cp\u003eA Notation.\u003c\/p\u003e \u003cp\u003eB Index of examples.\u003c\/p\u003e \u003cp\u003eC Software for Bayes linear computation.\u003c\/p\u003e \u003cp\u003eC.1 [B\/D].\u003c\/p\u003e \u003cp\u003eC.2 BAYES-LIN.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e  \u003cp\u003e“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.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2012)\u003c\/p\u003e \u003cp\u003e\"Summarizing, the book is an interesting compendium of methods of updating beliefs.\" (Stat Papers, 2010)\u003c\/p\u003e \u003cp\u003e\"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.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, November 2008)\u003c\/p\u003e \u003cp\u003e\"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.\" (\u003ci\u003eTechnometrics,\u003c\/i\u003e November 2008)\u003c\/p\u003e \u003cp\u003e\"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.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, September 2008)\u003c\/p\u003e  \u003cb\u003eMichael Goldstein,\u003c\/b\u003e \u003ci\u003eProfessor of Statistics, Department of Mathematical Sciences, University of Durham\u003c\/i\u003e\u003cbr\u003e 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”.  \u003cp\u003e\u003cb\u003eDavid Wooff,\u003c\/b\u003e \u003ci\u003eDirector of Statistics \u0026amp; Mathematics Consultancy Unit and Senior Lecturer in Statistics, Department of Mathematical Sciences, University of Durham\u003c\/i\u003e\u003cbr\u003e 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.\u003c\/p\u003e  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. \u003ci\u003eBayes Linear Statistics\u003c\/i\u003e presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.  \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eThe book covers:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eThe importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.\u003c\/li\u003e \u003cli\u003eSimple ways to use partial prior specifications to adjust beliefs, given observations.\u003c\/li\u003e \u003cli\u003eInterpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.\u003c\/li\u003e \u003cli\u003eGeneral approaches to statistical modelling based upon partial exchangeability judgements.\u003c\/li\u003e \u003cli\u003eBayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBayes Linear Statistics\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988794065125,"sku":"NP9780470015629","price":207.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470015629.jpg?v=1761781613","url":"https:\/\/k12savings.com\/es\/products\/bayes-linear-statistics-isbn-9780470015629","provider":"K12savings","version":"1.0","type":"link"}