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Poster
Infinite-Horizon Gaussian Processes
Arno Solin · James Hensman · Richard Turner

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #69
in Thu Poster Session A »

Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting long temporal datasets. State space modelling (Kalman filtering) enables these non-parametric models to be deployed on long datasets by reducing the complexity to linear in the number of data points. The complexity is still cubic in the state dimension m which is an impediment to practical application. In certain special cases (Gaussian likelihood, regular spacing) the GP posterior will reach a steady posterior state when the data are very long. We leverage this and formulate an inference scheme for GPs with general likelihoods, where inference is based on single-sweep EP (assumed density filtering). The infinite-horizon model tackles the cubic cost in the state dimensionality and reduces the cost in the state dimension m to O(m^2) per data point. The model is extended to online-learning of hyperparameters. We show examples for large finite-length modelling problems, and present how the method runs in real-time on a smartphone on a continuous data stream updated at 100 Hz.

Keywords: Gaussian Processes · Probabilistic Methods

Author Information

Arno Solin (Aalto University)
James Hensman (PROWLER.io)
Richard Turner (University of Cambridge)

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