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:
Initial Announcement on mloss.org.
Other available revisons
Version Changelog Date 3.5
- mechanism for specifying hyperparameter priors (together with Roman Garnett and José Vallet)
- new inference method inf/infGrid allowing efficient inference for data defined on a Cartesian grid (together with Andrew Wilson)
- new mean/cov functions for preference learning: meanPref/covPref
- new mean/cov functions for non-vectorial data: meanDiscrete/covDiscrete
- new piecewise constant nearest neighbor mean function: meanNN
- new mean functions being predictions from GPs: meanGP and meanGPexact
- new covariance function for standard additive noise: covEye
- new covariance function for factor analysis: covSEfact
- new covariance function with varying length scale : covSEvlen
- make covScale more general to scaling with a function instead of a scalar
- bugfix in covGabor* and covSM (due to Andrew Gordon Wilson)
- bugfix in lik/likBeta.m (suggested by Dali Wei)
- bugfix in solve_chol.c (due to Todd Small)
- bugfix in FITC inference mode (due to Joris Mooij) where the wrong mode for post.L was chosen when using infFITC and post.L being a diagonal matrix
- bugfix in infVB marginal likelihood for likLogistic with nonzero mean function (reported by James Lloyd)
- removed the combination likErf/infVB as it yields a bad posterior approximation and lacks theoretical justification
- Matlab and Octave compilation for L-BFGS-B v2.4 and the more recent L-BFGS-B v3.0 (contributed by José Vallet)
- smaller bugfixes in gp.m (due to Joris Mooij and Ernst Kloppenburg)
- bugfix in lik/likBeta.m (due to Dali Wei)
- updated use of logphi in lik/likErf
- bugfix in util/solve_chol.c where a typing issue occured on OS X (due to Todd Small)
- bugfix due to Bjørn Sand Jensen noticing that cov_deriv_sq_dist.m was missing in the distribution
- bugfix in infFITC_EP for ttau->inf (suggested by Ryan Turner)
December 8, 2014, 13:54:38 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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