Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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David Barber, Christopher Williams


The full Bayesian method for applying neural networks to a pre(cid:173) diction problem is to set up the prior/hyperprior structure for the net and then perform the necessary integrals. However, these inte(cid:173) grals are not tractable analytically, and Markov Chain Monte Carlo (MCMC) methods are slow, especially if the parameter space is high-dimensional. Using Gaussian processes we can approximate the weight space integral analytically, so that only a small number of hyperparameters need be integrated over by MCMC methods. We have applied this idea to classification problems, obtaining ex(cid:173) cellent results on the real-world problems investigated so far .