Efficient Implementation of Gaussian Processes
M N Gibbs and David J C MacKay
Neural networks and Bayesian inference
provide a useful framework within which to solve regression problems.
However their parameterisation 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 manipulation for
fixed values of hyperparameters.
We discuss in detail the workings of the method and
we detail a range of mathematical
and numerical techniques that are useful in applying Gaussian Processes to
general problems including efficient approximate matrix inversion methods
developed by Skilling.
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