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Abstract: Neural networks and Bayesian inference provide a useful framework within which to solve regression problems. However their parameterization means that the Bayesian analysis of neural networks can be difficult. In this paper, we investigate a method for regression using Gaussian process priors which allows exact Bayesian analysis using matrix manipulations. We discuss the workings of the method in detail. We will also detail a range of mathematical and numerical techniques that are useful in... (Update)
Context of citations to this paper: More
...neurons, the two were equivalent. It was also noted the linear models and radial basis functions were special cases of Gaussian processes [1]. Rasmussen showed that Gaussian processes were competitive on a number of benchmark problems [4] Here we look at the problems of non...
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BibTeX entry: (Update)
Gibbs, M. N. and MacKay, D. J. C. (1997a) "Efficient implementation of Gaussian processes ", draft manuscript. http://citeseer.ist.psu.edu/gibbs97efficient.html More
@misc{ gibbs-efficient,
author = "M. Gibbs and D. MacKay",
title = "Efficient implementation of Gaussian processes",
text = "Gibbs, M. N. and MacKay, D. J. C. (1997a) Efficient implementation of Gaussian
processes , draft manuscript.",
url = "citeseer.ist.psu.edu/gibbs97efficient.html" }
Citations (may not include all citations):335 Statistics for Spatial Data (context) - Cressie - 1993
4 Matrix Methods for Engineers and Scientists (context) - Barnett - 1979
1 and Plemmons (context) - Brown, Chu et al. - 1994
The graph only includes citing articles where the year of publication is known.
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