Dependent Gaussian Processes

Gaussian processes are usually parameterised in terms of their covariance functions. However, this makes it difficult to deal with multiple outputs, because ensuring that the covariance matrix is positive definite is problematic. An alternative formulation is to treat Gaussian processes as white noise sources convolved with smoothing kernels, and to parameterise the kernel instead. Using this, we extend Gaussian processes to handle multiple, coupled outputs.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Phillip Boyle, Marcus R. Frean
• Keywords: Covariance Functions, Covariance Matrix, Gaussian Processes, NIPS 2004, NIPS 2007