Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial

6 Feb 2014 Yarin Gal Mark van der Wilk

In this tutorial we explain the inference procedures developed for the sparse Gaussian process (GP) regression and Gaussian process latent variable model (GPLVM). Due to page limit the derivation given in Titsias (2009) and Titsias & Lawrence (2010) is brief, hence getting a full picture of it requires collecting results from several different sources and a substantial amount of algebra to fill-in the gaps... (read more)

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Gaussian Process
Non-Parametric Classification