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Robust Gaussian Process Regression and Applications

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The Gaussian Process Prior is very flexible, easy to use, and gives excellent results for many regression problems. As soon as we try to tackle real world data the non-gaussianity of nature and noise often makes it more difficult to adopt the GP scheme. I will discuss some non-Gaussian likelihood models to solve this in the context of an application to physiological heart-rate data.

This is work in progress.

This talk is part of the Inference Group series.

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