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Inference in Gaussian Process models

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Modeling partially unknown functions using examples is a common sub-task in many machine learning applications. Gaussian processes (GPs) are a convenient way to represent and manipulate distributions over functions. In some simple cases exact inference can be done in closed form, but generally approximation methods are required. In this talk I’ll give a brief introduction to GPs and give an overview of approximation techniques and their properties.

This talk is part of the Machine Learning and Inference (One day meeting) series.

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