A Practical Monte Carlo Implementation of Bayesian Learning

Carl Edward Rasmussen

Abstract: A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 data(cid:173) limited tasks from real world domains.