Indexed on: 01 Dec '95Published on: 01 Dec '95Published in: Statistics and Computing
We demonstrate how Bayes linear methods, based on partial prior specifications, bring us quickly to the heart of otherwise complex problems, giving us natural and systematic tools for evaluating our analyses which are not readily available in the usual Bayes formalism. We illustrate the approach using an example concerning problems of prediction in a large brewery. We describe the computer language [B/D] (an acronym for beliefs adjusted by data), which implements the approach. [B/D] incorporates a natural graphical representation of the analysis, providing a powerful way of thinking about the process of knowledge formulation and criticism which is also accessible to non-technical users.