Description:

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

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)
  • Monotonicity information
• 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
  • State space inference (1D for some covariance functions)
• 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

Changes to previous version:

2015-07-09 Version 4.6

Development and release branches available at https://github.com/gpstuff-dev/gpstuff

New features

• Use Pareto smoothed importance sampling (Vehtari & Gelman, 2015) for

• importance sampling leave-one-out cross-validation (gpmc_loopred.m)

• importance sampling integration over hyperparameters (gp_ia.m)

• importance sampling part of the logistic Gaussian process density estimation (lgpdens.m)

• references:

  • Aki Vehtari and Andrew Gelman (2015). Pareto smoothed importance sampling. arXiv preprint arXiv:1507.02646.
  • Aki Vehtari, Andrew Gelman and Jonah Gabry (2015). Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models.

• New covariance functions

  • gpcf_additive creates a mixture over products of kernels for each dimension reference: Duvenaud, D. K., Nickisch, H., & Rasmussen, C. E. (2011). Additive Gaussian processes. In Advances in neural information processing systems, pp. 226-234.
  • gpcf_linearLogistic corresponds to logistic mean function
  • gpcf_linearMichelismenten correpsonds Michelis Menten mean function

Improvements - faster EP moment calculation for lik_logit

Several minor bugfixes

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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