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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 and mean functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x.
- Changes to previous version:
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}
- 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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