About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

• mechanism for specifying hyperparameter priors (together with Roman Garnett and José Vallet)
• new inference method inf/infGrid allowing efficient inference for data defined on a Cartesian grid (together with Andrew Wilson)
• new mean/cov functions for preference learning: meanPref/covPref
• new mean/cov functions for non-vectorial data: meanDiscrete/covDiscrete
• new piecewise constant nearest neighbor mean function: meanNN
• new mean functions being predictions from GPs: meanGP and meanGPexact
• new covariance function for standard additive noise: covEye
• new covariance function for factor analysis: covSEfact
• new covariance function with varying length scale : covSEvlen
• make covScale more general to scaling with a function instead of a scalar
• bugfix in covGabor* and covSM (due to Andrew Gordon Wilson)
• bugfix in lik/likBeta.m (suggested by Dali Wei)
• bugfix in solve_chol.c (due to Todd Small)
• bugfix in FITC inference mode (due to Joris Mooij) where the wrong mode for post.L was chosen when using infFITC and post.L being a diagonal matrix
• bugfix in infVB marginal likelihood for likLogistic with nonzero mean function (reported by James Lloyd)
• removed the combination likErf/infVB as it yields a bad posterior approximation and lacks theoretical justification
• Matlab and Octave compilation for L-BFGS-B v2.4 and the more recent L-BFGS-B v3.0 (contributed by José Vallet)
• smaller bugfixes in gp.m (due to Joris Mooij and Ernst Kloppenburg)
• bugfix in lik/likBeta.m (due to Dali Wei)
• updated use of logphi in lik/likErf
• bugfix in util/solve_chol.c where a typing issue occured on OS X (due to Todd Small)
• bugfix due to Bjørn Sand Jensen noticing that cov_deriv_sq_dist.m was missing in the distribution
• bugfix in infFITC_EP for ttau->inf (suggested by Ryan Turner)

About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

2014-07-22 Version 4.5

New features

• Input dependent noise and signal variance.

  • Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Expectation Propagation for Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication. Preprint http://arxiv.org/abs/1404.5443

• Sparse stochastic variational inference model.

  • Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes for big data. arXiv preprint http://arxiv.org/abs/1309.6835.

• Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the posterior predictive distribution of the latent variable.

  • Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, 57(6):1317-1339.

  • Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution and Asymmetric Principal Components Analysis. Communications in Statistics - Theory and Methods, 35(6):1123-1140.

Improvements

• New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
• Precomputed demo results (including the figures) are now available in the folder tests/realValues.
• New demos demonstrating new features etc.
  • demo_epinf, demonstrating the input dependent noise and signal variance model
  • demo_svi_regression, demo_svi_classification
  • demo_modelcomparison2, demo_survival_comparison

Several minor bugfixes