Predictive mean, variance and log marginal likelihood of a GP. See "2.3 Varying the Hyperparameters" on page 19 of Rasmussen and Williams' book.
1 | gp.predict(y, K = NULL, Kstar, Kstarstar, U = chol(K))
|
y |
The targets. |
K |
The covariance matrix (kernel) for input points, not needed if U is provided. |
Kstar |
The cross covariance matrix (kernel) |
Kstarstar |
The cross covariance matrix (kernel) for test points |
U |
Cholesky decomposition of K |
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