Multiple Output Gaussian Process Regression


Authors: Phillip Boyle, Marcus Frean
Source: GZipped PostScript (480kb); Adobe PDF (398kb)

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 parameterise the kernel instead. Using this, we extend Gaussian processes to handle multiple, coupled outputs.

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