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Robust Gaussian Process Regression and Applications

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If you have a question about this talk, please contact Oliver Stegle.

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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