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.