Gaussian Processes for Machine Learning¶
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Bayesian Interpretation of Kernel Regularization
- When kernels, means, scale parameters, and normalization conventions are matched, the RKHS minimizer equals the GP posterior mean
This sourceChapter 6.2 derives the regularization-network solution and observes that it is exactly the GP predictive mean, with the regularization weight tied to the noise variance - the standard statement of the equality this sentence asserts.
SupportedVerified against the source
- When kernels, means, scale parameters, and normalization conventions are matched, the RKHS minimizer equals the GP posterior mean
- Kriging
- Without Gaussianity, kriging retains its best-linear-unbiased interpretation but not a full posterior-distribution equivalence.
This sourceAuthoritative reference for Gaussian-process priors, covariance functions, observation-noise models, conditioning, posterior means, and posterior variances.
- Without Gaussianity, kriging retains its best-linear-unbiased interpretation but not a full posterior-distribution equivalence.
Mechanisms¶
- Gaussian Process Function Model
- A Gaussian process (in geostatistics this is called kriging) models grade as a smooth random field: the covariance kernel encodes that two nearby points have similar grade, with similarity decaying over distance.
This sourceExplains Gaussian-process covariance kernels in which correlation decreases as the distance between inputs increases.
- A Gaussian process (in geostatistics this is called kriging) models grade as a smooth random field: the covariance kernel encodes that two nearby points have similar grade, with similarity decaying over distance.
Verification¶
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Links previously used in the corpus¶
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Registry ID ref:c55bf78b0959 · see in the full table