covarFun.LatentPlusNoise: Augment 'CovarFun' with latent function and noise...
In JimSkinner/gpclassifier: Implements a Gaussian Process binary Classifier
Description
Usage
Arguments
Value
Examples
View source: R/CovarianceFunction.R
Description
Creates a covariance function k which augments some covariance function k_f
with two extra hyperparameters lsf and lsn such that:
k(x, y) = exp(lsf)*k_f(x, y) + exp(lsn)*I[x=y]
Automatically provides derivatives of lsf, lsn. Used for neater covariance
funtion specifications. lsf, lsn stand for log(sigma^2_f) and log(sigma^2_n),
where 'f' labels the magnitude parameter and kernel for the latent
function, whilst 'n' labels the magnitude parameter for the noise.
Usage
1
Arguments
covarFun
CovarFun object to augment.
Value
covarFun augmented with lsf, lsn hyperparameterss for signal and
noise magnitude.
Examples
1
JimSkinner/gpclassifier documentation built on May 7, 2019, 10:52 a.m.
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Description Usage Arguments Value Examples
View source: R/CovarianceFunction.R
Description
Creates a covariance function k which augments some covariance function k_f with two extra hyperparameters lsf and lsn such that: k(x, y) = exp(lsf)*k_f(x, y) + exp(lsn)*I[x=y] Automatically provides derivatives of lsf, lsn. Used for neater covariance funtion specifications. lsf, lsn stand for log(sigma^2_f) and log(sigma^2_n), where 'f' labels the magnitude parameter and kernel for the latent function, whilst 'n' labels the magnitude parameter for the noise.
Usage
1 |
Arguments
covarFun |
|
Value
covarFun augmented with lsf, lsn hyperparameterss for signal and noise magnitude.
Examples
1 |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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