Chapter The Bayesian Paradigm second generation neural computing

When reasoning in the presence of uncertainty there is a unique and self consistent set of rules for induction and model selection Bayesian inference Recent advances in neural networks have been fuelled by the adoption of this Bayesian framework either implicitly for example through the use of commit tees or explicitly through Bayesian evidence and sampling frameworks In this chapter we show how this second generation of neural network techniques can be applied to biomedical data and focus on the networks ability to provide assessments of the condence associated with its predictions This is an essen tial requirement for any automatic biomedical pattern recognition system It allows low condence decisions to be highlighted and deferred possibly to a human expert and falls naturally out of the Bayesian framewor