Goldstein / Wooff Bayes Linear Statistics

Theory and Methods
1. Auflage 2007
ISBN: 978-0-470-06567-9
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 536 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

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.

Autoren/Hrsg.

Goldstein, Michael

Wooff, David

Inhaltsverzeichnis

Preface.
1 The Bayes linear approach.
2 Expectation.
3 Adjusting beliefs.
4 The observed adjustment.
5 Partial Bayes linear analysis.
6 Exchangeable beliefs.
7 Co-exchangeable beliefs.
8 Learning about population variances.
9 Belief comparison.
10 Bayes linear graphical models.
11 Matrix algebra for implementing the theory.
12 Implementing Bayes linear statistics.
A Notation.
B Index of examples.
C Software for Bayes linear computation.
C.1 [B/D].
C.2 BAYES-LIN.
References.
Index.

Über Autor(innen)

Michael Goldstein, Professor of Statistics, Department ofMathematical Sciences, University of Durham
Michael Goldstein has worked on and researched the Bayes linearapproach for around 30 years, his general interests being in thefoundations, methodology and applications of Bayesian/subjectivistapproaches to statistics. He has an outstanding reputation as oneof the most original thinkers in the field, and was a contributingauthor to Wiley's "Encyclopedia of StatisticalSciences".
David Wooff, Director of Statistics & MathematicsConsultancy Unit and Senior Lecturer in Statistics, Department ofMathematical Sciences, University of Durham
David Wooff has been involved in a long collaboration for over 20years with Michael Goldstein and others on developing Bayes linearmethods, his primary research interest being the generaldevelopment and application of Bayes linear methodology.