Gaussian processes for Bayesian classification via Hybrid Monte Carlo


The full Bayesian method for applying neural networks to a prediction problem is to set up the prior/hyperprior structure for the net and then perform the necessary integrals. However, these integrals 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 excellent results on the real-world problems investigated so far.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Event Title: 10th Annual Conference on Neural Information Processing Systems, NIPS 1996
Event Type: Other
Event Dates: 1996-12-02 - 1996-12-05
Uncontrolled Keywords: Bayesian method,neural networks,structure for the net,integrals,Markov Chain Monte Carlo,weight space integral,Computer Networks and Communications,Information Systems,Signal Processing
ISBN: 0262100657
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://mitpress ... type=2&tid=3990 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 1997-05
Authors: Barber, David
Williams, Christopher K. I.



Version: Published Version

| Preview

Export / Share Citation


Additional statistics for this record