Corpus ID: 88513308

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

@article{Gal2014VariationalII,
  title={Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial},
  author={Y. Gal and Mark van der Wilk},
  journal={arXiv: Machine Learning},
  year={2014}
}
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. Our main goal is thus to collect all the results and full derivations into one place to… Expand
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A Unifying View of Sparse Approximate Gaussian Process Regression
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