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.
Changes:
2014-07-22 Version 4.5
New features
Input dependent noise and signal variance.
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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.
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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
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New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
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Precomputed demo results (including the figures) are now available in the folder tests/realValues.
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New demos demonstrating new features etc.
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demo_epinf, demonstrating the input dependent noise and signal variance model
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demo_svi_regression, demo_svi_classification
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demo_modelcomparison2, demo_survival_comparison
Several minor bugfixes
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