Truncated Covariance Matrices and Toeplitz Methods in Gaussian Processes
نویسنده
چکیده
Gaussian processes are a limit extension of neural networks. Standard Gaussian process techniques use a squared exponential covariance function. Here, the use of truncated covariances is proposed. Such cov-ariances have compact support. Their use speeds up matrix inversion and increases precision. Furthermore they allow the use of speedy, memory eecient Toeplitz inversion for high dimensional grid based Gaus-sian process predictors.
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