Project details for GPML Gaussian Processes for Machine Learning Toolbox

Screenshot GPML Gaussian Processes for Machine Learning Toolbox 3.0

by hn - July 23, 2010, 12:13:58 CET [ Project Homepage BibTeX Download ]

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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

BibTeX Entry: Download
URL: Project 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
  • added a new inference function infGrid_Laplace allowing to use non-Gaussian likelihoods for large grids

  • fixed a bug due to Octave evaluating norm([]) to a tiny nonzero value, modified all lik/lik*.m functions reported by Philipp Richter

  • small bugfixes in covGrid and infGrid

  • bugfix in predictive variance of likNegBinom due to Seth Flaxman

  • bugfix in infFITC_Laplace as suggested by Wu Lin

  • bugfix in covPP{iso,ard}

July 6, 2015, 12:31:28
  • 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
  • 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
  • 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

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

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

Initial Announcement on

July 23, 2010, 12:13:58


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