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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:
- 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
- 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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