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, multinomial logit (softmax), multinomial probit
  • 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
  • marginal posterior corrections (cm2 and fact)
  • 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, 14:1175-1179.

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

Changes to previous version:

2013-04-24 Version 4.1

New features:

• Multinomial probit classification with nested-EP. Jaakko Riihimäki, Pasi Jylänki and Aki Vehtari (2013). Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood. Journal of Machine Learning Research 14:75-109, 2013.
• Marginal posterior corrections for latent values. Cseke & Heskes (2011). Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research 12 (2011), 417-454
  • Laplace: cm2 and fact
  • EP: fact

Improvements

• lgpdens ignores now NaNs instead of giving error
• gp_cpred has a new option 'target' accpeting values 'f' or 'mu'
• unified gp_waic and gp_dic
  • by default return mlpd
  • option 'form' accetps now values 'mean' 'all' 'sum' and 'dic'
• improved survival demo demo_survival_aft (accalerated failure time)
  • renamed and improved from demo_survival_weibull
• rearranged some files to more logical directories
• bug fixes

New files

• gp_predcm: marginal posterior corrections for latent values.
• demo_improvedmarginals: demonstration of marginal posterior corrections
• demo_improvedmarginals2: demonstration of marginal posterior corrections
• lik_multinomprobit: multinomial probit likelihood
• demo_multiclass_nested_ep: demonstration of nested EP with multinomprobit

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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