Gaussian Processes offer a flexible framework for Bayesian non-parametric regression. This makes them appealing, until one is stuck trying to invert Gigabytes of covariance matrix. The talk will offer no new algorithms. Instead I will describe existing methods that work, and those that — despite papers written to the contrary — demonstrably don’t.
