Project details for GPML Gaussian Processes for Machine Learning Toolbox

Screenshot JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.3

by hn - October 22, 2013, 15:34:05 CET [ Project Homepage BibTeX Download ]

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Description:

The GPML toolbox implements approximate inference algorithms for Gaussian processes such as Expectation Propagation, the Laplace Approximation and Variational Bayes for a wide class of likelihood functions for both regression and classification. It comes with a big algebra of covariance and mean functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x.

Changes to previous version:
  • new generalised linear model likelihoods: gamma, beta, inverse Gaussian
  • new ard/iso covariances: covPPard, covMaternard, covLINiso
  • new spectral covariances: covSM, covGaboriso and covGaborard
  • new meta covariance to turn an arbitrary stationary covariance into a periodic covariance one: covPERard, covPERiso
  • new periodic covariance with zero DC component and correct scaling: covPeriodicNoDC, covCos
  • new variational inference approximation based on direct KL minimisation: infKL
  • improved inf/infVB double loop scheme so that only very few likelihood properties are required; infVB is now internally a sequence of infLaplace runs
  • improved inf/infLaplace to be more generic so that optimisers other than scaled Newton can be used
  • improved inf/infEP so that the internal variables (mu,Sigma) now represent the current posterior approximation
BibTeX Entry: Download
URL: Project Homepage
JMLR MLOSS PaperURL: JMLR-MLOSS Paper Homepage
Supported Operating Systems: Agnostic, Platform Independent
Data Formats: Matlab, Octave
Tags: Classification, Regression, Approximate Inference, Gaussian Processes
Archive: download here

Other available revisons

Version Changelog Date
3.4
  • derivatives w.r.t. inducing points xu in infFITC, infFITC_Laplace, infFITC_EP so that one can treat the inducing points either as fixed given quantities or as additional hyperparameters
  • new GLM likelihood likExp for inter-arrival time modeling
  • new GLM likelihood likWeibull for extremal value regression
  • new GLM likelihood likGumbel for extremal value regression
  • new mean function meanPoly depending polynomially on the data
  • infExact can deal safely with the zero noise variance limit
  • support of GP warping through the new likelihood function likGaussWarp
November 11, 2013, 14:46:52
3.3
  • new generalised linear model likelihoods: gamma, beta, inverse Gaussian
  • new ard/iso covariances: covPPard, covMaternard, covLINiso
  • new spectral covariances: covSM, covGaboriso and covGaborard
  • new meta covariance to turn an arbitrary stationary covariance into a periodic covariance one: covPERard, covPERiso
  • new periodic covariance with zero DC component and correct scaling: covPeriodicNoDC, covCos
  • new variational inference approximation based on direct KL minimisation: infKL
  • improved inf/infVB double loop scheme so that only very few likelihood properties are required; infVB is now internally a sequence of infLaplace runs
  • improved inf/infLaplace to be more generic so that optimisers other than scaled Newton can be used
  • improved inf/infEP so that the internal variables (mu,Sigma) now represent the current posterior approximation
October 22, 2013, 15:34:05
3.2

We now support inference on large datasets using the FITC approximation for non-Gaussian likelihoods for EP and Laplace's approximation. New likelihood functions: mixture likelihood, Poisson likelihood, label noise. We added two MCMC samplers.

January 21, 2013, 15:34:50
3.1

We now support inference on large datasets using the FITC approximation by Ed Snelson. The covariance function interface had to be slightly modified.

September 28, 2010, 05:51:56
3.0

Initial Announcement on mloss.org.

July 23, 2010, 12:13:58

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