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 hostingfree software and
guides to the calculations within the book.
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