Description
Book SynopsisBayesian 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.
Trade Review“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)
Table of ContentsPreface.
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.
