Gaussian Processes for Big Data

Understand

We introduce stochastic variational inference for Gaussian process models.

• This enables the application of Gaussian process (GP) models to data sets containing millions of data points.
• We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference.
• Our ap- proach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes.