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Treed Gaussian Processes for Regression and Classification

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A Gaussian process is a popular nonparametric model for regression and classification that specifies a prior over functions. It is often constructed so that this distribution over functions is stationary although many data sets exhibit only local stationarity. A treed Gaussian process is an efficient nonstationary modeling scheme that fits stationary Gaussian processes to regions of a treed partition. This partition not only allows a more general, interpretable model but also facilitates faster internal Gaussian process calculations, as for prediction. The treed model has recently been developed and applied to regression problems. Here, it is extended to solve classification problems.

This talk is part of the Inference Group series.

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