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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:
We now support inference on large datasets using the FITC approximation by Ed Snelson. The covariance function interface had to be slightly modified.
- 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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