Deep Gaussian Covariance Network

Understand

The correlation length-scale next to the noise variance are the most used hyperparameters for the Gaussian processes.

• Typically, stationary covariance functions are used, which are only dependent on the distances between input points and thus invariant to the translations in the input space.
• The optimization of the hyperparameters is commonly done by maximizing the log marginal likelihood.
• This works quite well, if the distances are uniform distributed.