This is a
MATLAB implementation of the "marginal GP" (MGP) described
Garnett, R., Osborne, M., and Hennig, P. Active Learning of Linear Embeddings for Gaussian Processes. (2014). 30th Conference on Uncertainty in Artificial Intellignece (UAI 2014).
where are the hyperparameters of the model. Suppose we have a dataset of observations and a test point . This function returns the mean and variance of the approximate marginal predictive distributions for the associated observation value and latent function value :
This code is only appropriate for GP regression! Exact inference with a Gaussian observation likelihood is assumed.
The MGP approximation requires that the provided hyperparameters be the MLE hyperparameters:
This code is written to be interoperable with the GPML MATLAB toolbox, available here:
The GPML toolbox must be in your MATLAB path for this function to
work. This function also depends on the
which must also be in your MATLAB path.
The usage of
mgp.m is identical to the
gp.m function from the GPML
toolkit in prediction mode. See
mgp.m for more information.
A demo is provided in