
 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: JMLRMLOSS 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 interarrival 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 nonGaussian 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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