About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.

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

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes

generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used