A Practical Monte Carlo Implementation of Bayesian Learning

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Carl Rasmussen


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.