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[PyMCon] Introduction to Hilbert Space Gaussian Processes in PyMC

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Christian L.
[PyMCon] Introduction to Hilbert Space Gaussian Processes in PyMC


PyMCon Web Series: Introduction to Hilbert Space GPs (HSGP) in PyMC: A fast Gaussian process approximation that you can actually use

March 15, 22:00 UTC
March 15, 6pm New York
March 15, 3pm Los Angeles
March 16, 9am Sydney
Feb 22nd, 7am Tokyo

Bill Engels

Abstract of the talk
Gaussian processes (GPs) are a versatile tool in the Bayesian modelers toolbox - in theory. In practice, for all but the smallest data sets, one needs to resort to approximations to actually fit GPs in any reasonable amount of time. There are currently a few GP approximations implemented in PyMC based on inducing points that are fast, but only apply when the likelihood is Gaussian. The Hilbert Space Gaussian Process (HSGP) approximation works well with any likelihood and scales as O(nm + m). In this talk I'll introduce a PyMC HSGP implementation and show via case studies how it fills a few key gaps in the PyMC GP library: fast GPs as model subcomponents, and fast GPs with non-Gaussian likelihoods. I'll also cover tips and tricks for applying HSGPs effectively in practice.

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