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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:
Logdet-estimation functionality for grid-based 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
Conjugate-Computation Variational Inference algorithm
much more scalable than previous versions
Time-series covariance functions on the positive real line
covW (i-times integrated) Wiener process covariance
covOU (i-times integrated) Ornstein-Uhlenbeck 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 non-integer 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
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