Variational Gaussian Process

@article{Tran2015VariationalGP,
  title={Variational Gaussian Process},
  author={Dustin Tran and Rajesh Ranganath and David M. Blei},
  journal={CoRR},
  year={2015},
  volume={abs/1511.06499}
}
Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned… CONTINUE READING
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