Evaluation of Neural Network Models
This chapter reports empirical evaluations of the predictive performance of Bayesian neural network models applied to several synthetic and real data sets. Good results were obtained when large networks with appropriate priors were used on small data sets for a synthetic regression problem, confirming expectations based on properties of the associated priors over functions. The Automatic Relevance Determination model was effective in suppressing irrelevant inputs in tests on synthetic regression and classification problems. Tests on two real data sets showed that Bayesian neural network models, implemented using hybrid Monte Carlo, can produce good results when applied to realistic problems of moderate size.