Description:

GPstuff is a toolbox for Bayesian Modeling with Gaussian Processes with following features and more: - Several covariance functions (e.g. squared exponential, exponential, Matérn, periodic and a compactly supported piece wise polynomial function) - Sums, products and scaling of covariance functions - Euclidean and delta distance - Several mean functions with marginalized parameters - Several likelihood/observation models - Continuous observations: Gaussian, Gaussian scale mixture (MCMC only), Student's-t, quantile regression - Classification: Logit, Probit, Softmax - Count data: Binomial, Poisson, (Zero truncated) Negative-Binomial, Hurdle model, Zero-inflated Negative-Binomial, Multinomial - Survival: Cox-PH, Weibull, log-Gaussian, log-logistic - Point process: Log-Gaussian Cox process - Density estimation and regression: logistic GP - Other: derivative observations (for sexp covariance function only) - Hierarchical priors for hyperparameters - Sparse models - Sparse matrix routines for compactly supported covariance functions - Fully and partially independent conditional (FIC, PIC) - Compactly supported plus FIC (CS+FIC) - Variational sparse (VAR), Deterministic training conditional (DTC), Subset of regressors (SOR) (Gaussian/EP only) - PASS-GP - Latent inference - Exact (Gaussian only) - Laplace, Expectation propagation (EP), Parallel EP, Robust-EP - Scaled Metropolis, Hamiltonian Monte Carlo (HMC), Scaled HMC, Elliptical slice sampling - Hyperparameter inference - Type II ML/MAP - Leave-one-out cross-validation (LOO-CV), Laplace/EP LOO-CV - Metropolis, HMC, No-U-Turn-Sampler (NUTS), Slice Sampling (SLS), Surrogate SLS, Shrinking-rank SLS, Covariance-matching SLS - Grid, CCD, Importance sampling - Model assessment - LOO-CV, Laplace/EP LOO-CV, IS-LOO-CV, k-fold-CV - WAIC, DIC - Average predictive comparison

If you use GPstuff, please use the reference: Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari (2013). GPstuff: Bayesian Modeling with Gaussian Processes. In Journal of Machine Learning Research, in press.

See also user guide at http://arxiv.org/abs/1206.5754v3

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic, Platform Independent

Data Formats: Matlab, Octave

Tags: Classification, Regression, Machine Learning, Nonparametric Bayes, Gaussian Process, Bayesian Inference

Archive: download here

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