Variable Architecture Bayesian Neural Networks: Model Selection Based on EMC.

This work addresses the problem of Selecting appropriate architectures for Bayesian Neural Networks (BNN). Specifically, it proposes a variable architecture model where the number of hidden units are selected by using a variant of the real-coded Evolutionary Monte Carlo algorithm developed by Liang and Wong (2001) for inference and prediction in fixed architecture Bayesian Neural Networks. [ABSTRACT FROM AUTHOR]