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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, likelihood, mean and hyperprior functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x.
- Changes to previous version:
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 grid-based 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, grid-based 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 dot-product-based covariances and
covMaha for Mahalanobis-distance-based 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
two-fold speedup of util/elsympol used by covADD by Truong X. Nghiem
bugfix in util/logphi as reported by John Darby
- BibTeX Entry: Download
- Supported Operating Systems: Agnostic, Platform Independent
- Data Formats: Matlab, Octave
- Tags: Classification, Regression, Approximate Inference, Gaussian Processes
- Archive: download here
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