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

Description Usage Arguments

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

1	cov.noisy.RQ(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 log alpha, and beta[4] is the variance of the noise.