Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo
Abstract
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 .
Cite
Text
Barber and Williams. "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo." Neural Information Processing Systems, 1996.Markdown
[Barber and Williams. "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/barber1996neurips-gaussian/)BibTeX
@inproceedings{barber1996neurips-gaussian,
title = {{Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo}},
author = {Barber, David and Williams, Christopher K. I.},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {340-346},
url = {https://mlanthology.org/neurips/1996/barber1996neurips-gaussian/}
}