New Zealand Statistical Association
2004 Conference

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Marcus Frean

Dependent Gaussian Processes for Regression

Joint work with Phillip Boyle, MCS, VUW.

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.