Project details for GPstuff

Screenshot GPstuff 4.1

by avehtari - April 25, 2013, 11:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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

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


  • 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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