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2018

Scalable L\'evy Process Priors for Spectral Kernel Learning

Jang, Phillip A., Loeb, Andrew E., Davidow, Matthew B. et al.

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Understand

Gaussian processes are rich distributions over functions, with generalization properties determined by a kernel function.

When used for long-range extrapolation, predictions are particularly sensitive to the choice of kernel parameters.
It is therefore critical to account for kernel uncertainty in our predictive distributions.
We propose a distribution over kernels formed by modelling a spectral mixture density with a L\'evy process.

Scalable L\'evy Process Priors for Spectral Kernel Learning · Around

Built on

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Ideal spatial adaptation by wavelet shrinkage
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Reversible jump monte carlo computation and bayesian model determination
Green, P.J · 1995
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Bayesian Learning for Neural Networks
Neal, R.M · 1996
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Introduction to Gaussian processes
MacKay, David J.C · 1998
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