  In recent years there has been interest in the theory of local computation
over probabilistic Bayesian graphical models. In this paper, local computation
over Bayes linear belief networks is shown to be amenable to a similar
approach. However, the linear structure offers many simplifications and
advantages relative to more complex models, and these are examined with
reference to some illustrative examples.
