Description:

This code contains python 2.7 and 3.x implementations of the Extended and Unscented Gaussian Processes (E/UGPs) for solving inverse problems. These are similar to regular GPs, but the latent function, f, can optionally have an extra nonlinear relationship to the observations, y, in the likelihood,

• Normal GP likelihood: y ~ N(f, s^2 I_N) or for a single observation, n, y_n = f_n + e.
• E/UGP likelihood: y ~ N(g(f), s^2 I_N) or for a single observation, n, y_n = g(f_n) + e.

Where g(.) is an arbitrary scalar function (maps R to R). The posterior Gaussian process parameters are learned using a variational objective with Gauss-Newton style linearization (of g) and mean finding.

More information can be found in our NIPS 2014 paper here:

http://papers.nips.cc/paper/5455-extended-and-unscented-gaussian-processes

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Mac Os X

Data Formats: Numpy

Tags: Regression, Approximate Inference, Nonparametric Bayes, Variational Inference, Inverse Methods, Gaussian Process, Bayesian Inference, Black Box Optimization, Generalized Regression, Derivative Free

Archive: download here

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