
 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, likelihood, mean and hyperprior functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x.
 Changes to previous version:
Logdetestimation functionality for gridbased approximate covariances
Lanczos subspace estimation
Chebyshef polynomial expansion
More generic infEP functionality
dense computations and sparse approximations using the same code
covering KL inference as a special cas of EP
New infKL function contributed by Emtiyaz Khan and Wu Lin
ConjugateComputation Variational Inference algorithm
much more scalable than previous versions
Timeseries covariance functions on the positive real line
covW (itimes integrated) Wiener process covariance
covOU (itimes integrated) OrnsteinUhlenbeck process covariance (contributed by Juan Pablo Carbajal)
covULL underdamped linear Langevin process covariance (contributed by Robert MacKay)
covFBM Fractional Brownian motion covariance
New covariance functions
covWarp implements k(w(x),w(z)) where w is a "warping" function
covMatern has been extended to also accept noninteger distance parameters
 BibTeX Entry: Download
 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 4.1 Logdetestimation functionality for gridbased approximate covariances
Lanczos subspace estimation
Chebyshef polynomial expansion
More generic infEP functionality
dense computations and sparse approximations using the same code
covering KL inference as a special cas of EP
New infKL function contributed by Emtiyaz Khan and Wu Lin
ConjugateComputation Variational Inference algorithm
much more scalable than previous versions
Timeseries covariance functions on the positive real line
covW (itimes integrated) Wiener process covariance
covOU (itimes integrated) OrnsteinUhlenbeck process covariance (contributed by Juan Pablo Carbajal)
covULL underdamped linear Langevin process covariance (contributed by Robert MacKay)
covFBM Fractional Brownian motion covariance
New covariance functions
covWarp implements k(w(x),w(z)) where w is a "warping" function
covMatern has been extended to also accept noninteger distance parameters
November 27, 2017, 19:26:13 4.0 A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include gridbased covariance approximations natively.
More generic sparse approximation using Power EP
unified treatment of FITC approximation, variational approaches VFE and hybrids
inducing input optimisation for all (compositions of) covariance functions dropping the previous limitation to a few standard examples
infFITC is now covered by the more generic infGaussLik function
Approximate covariance object unifying sparse approximations, gridbased approximations and exact covariance computations
implementation in cov/apx, cov/apxGrid, cov/apxSparse
generic infGaussLik unifies infExact, infFITC and infGrid
generic infLaplace unifies infLaplace, infFITC_Laplace and infGrid_Laplace
Hiearchical structure of covariance functions
clear hierachical compositional implementation
no more code duplication as present in covSEiso and covSEard pairs
two mother covariance functions
covDot for dotproductbased covariances and
covMaha for Mahalanobisdistancebased covariances
a variety of modifiers: eye, iso, ard, proj, fact, vlen
more flexibility as more variants are available and possible
all covariance functions offer derivatives w.r.t. inputs
Faster derivative computations for mean and cov functions
switched from partial derivatives to directional derivatives
simpler and more concise interface of mean and cov functions
much faster marginal likelihood derivative computations
simpler and more compact code
New mean functions
new mean/meanWSPC (Weighted Sum of Projected Cosines or Random Kitchen Sink features) following a suggestion by William Herlands
new mean/meanWarp for constructing a new mean from an existing one by means of a warping function adapted from William Herlands
New optimizer
 added a new minimize_minfunc, contributed by Truong X. Nghiem
New GLM link function
 added the twice logistic link function util/glm_invlink_logistic2
Smaller fixes
twofold speedup of util/elsympol used by covADD by Truong X. Nghiem
bugfix in util/logphi as reported by John Darby
October 19, 2016, 10:15:05 3.6 added a new inference function infGrid_Laplace allowing to use nonGaussian 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 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 nonvectorial 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 LBFGSB v2.4 and the more recent LBFGSB 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 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
Comments
No one has posted any comments yet. Perhaps you'd like to be the first?
Leave a comment
You must be logged in to post comments.