cov.noisy.SE: Noisy SE covariance function
In JimSkinner/spca: Structured PCA

Description Usage Arguments

The sum of the Squared Exponential and the independent covariance functions. Interpreted as the smooth SE covariance function plus iid noise. This is implemented as the sum of two covariance functions, with the first two elements of beta being sent to the SE covariance function, and the last element being sent to the independent covariance function.

1	cov.noisy.SE(X, X2, beta, D = NA, ...)

`X`	Matrix of data
`X2`	(optional) second matrix of data; if omitted, X is used.
`beta`	Hyperparameters; beta[1] is the log signal variance, beta[2] is the log length scale, beta[3] is the variance of the noise.