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:

2016-06-09 Version 4.7

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

New features

• Simple Bayesian Optimization demo

Improvements

• Improved use of PSIS
• More options added to gp_monotonic
• Monotonicity now works for additive covariance functions with selected variables
• Possibility to use gpcf_squared.m-covariance function with derivative observations/monotonicity
• Default behaviour made more robust by changing default jitter from 1e-9 to 1e-6
• LA-LOO uses the cavity method as the default (see Vehtari et al (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. JMLR, accpeted for publication)
• Selected variables -option works now better with monotonicity

Bugfixes

• small error in derivative observation computation fixed
• several minor bug fixes

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

Other available revisons

Version	Changelog	Date
4.7	2016-06-09 Version 4.7 Development and release branches available at https://github.com/gpstuff-dev/gpstuff New features Simple Bayesian Optimization demo Improvements Improved use of PSIS More options added to gp_monotonic Monotonicity now works for additive covariance functions with selected variables Possibility to use gpcf_squared.m-covariance function with derivative observations/monotonicity Default behaviour made more robust by changing default jitter from 1e-9 to 1e-6 LA-LOO uses the cavity method as the default (see Vehtari et al (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. JMLR, accpeted for publication) Selected variables -option works now better with monotonicity Bugfixes small error in derivative observation computation fixed several minor bug fixes	June 9, 2016, 17:45:15
4.6	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	July 15, 2015, 15:08:06
4.5	2014-07-22 Version 4.5 New features Input dependent noise and signal variance. 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. 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 New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported). Precomputed demo results (including the figures) are now available in the folder tests/realValues. New demos demonstrating new features etc. demo_epinf, demonstrating the input dependent noise and signal variance model demo_svi_regression, demo_svi_classification demo_modelcomparison2, demo_survival_comparison Several minor bugfixes	July 22, 2014, 14:03:11
4.4	2014-04-11 Version 4.4 New features Monotonicity constraint for the latent function. Riihimäki and Vehtari (2010). Gaussian processes with monotonicity information. Journal of Machine Learning Research: Workshop and Conference Proceedings, 9:645-652. State space implementation for GP inference (1D) using Kalman filtering. For the following covariance functions: Squared-Exponential, Matérn-3/2 & 5/2, Exponential, Periodic, Constant Särkkä, S., Solin, A., Hartikainen, J. (2013). Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing. IEEE Signal Processing Magazine, 30(4):51-61. Simo Sarkka (2013). Bayesian filtering and smoothing. Cambridge University Press. Solin, A. and Särkkä, S. (2014). Explicit link between periodic covariance functions and state space models. AISTATS 2014. Improvements GP_PLOT function for quick plotting of GP predictions GP_IA now warns if it detects multimodal posterior distributions much faster EP with log-Gaussian likelihood (numerical integrals -> analytical results) faster WAIC with GP_IA array (numerical integrals -> analytical results) New demos demonstrating new features etc. demo_minimal, minimal demo for regression and classification demo_kalman1, demo_kalman2 demo_monotonic, demo_monotonic2 Plus bug fixes	April 15, 2014, 15:26:49
4.3.1	2013-11-26 Version 4.3.1 Improvements: Updated cpsrf and psrf to follow BDA3: split each chain to two halves and use Geyer's IPSE for n_eff Multi-latent models for Octave	November 29, 2013, 14:10:59
4.3	2013-10-14 Version 4.3 Improvements: lgpdens.m: better default estimation using importance and rejection sampling, better default priors (see updated paper http://arxiv.org/abs/1211.0174) Robust-EP for zero truncated negative-binomial likelihood If moment computations in EP return NaN, return NaN energy (handled gracefully by fminlbfgs and fminscg) gp_cpred.m: new option 'target' gp_ia.m: Changed Hessian computation stepsize to 1e-3 gpstuff_version.m: function for returning current GPstuff version gpia_jpreds.m: a new function demo_survival_weibull.m -> demo_survival_aft.m Bug fixes: build suitesparse path correctly if it includes spaces gp_avpredcomp.m: fixed for Cox-PH gp_cpred.m: fixed for Cox-PH esls.m: don't accept a step to a point with infinite log likelihood gp_ia.m: removed some redundant computation gp_rnd.m: works now for multilatent models also bugfixes for setrandstream other bugfixes	October 16, 2013, 13:27:09
4.2	2013-06-14 Version 4.2 Improvements Cross-validation much faster if no bias-corrections are needed (computes only the necessary predictions) Marginal posterior corrections with loopred (Laplace) and cross-validation More robust computation of marginal posterior corrections More robust density estimation in lgpdens (default parameters changed) Bug fixes Mex files now in correct folders if compiled with SuiteSparse (covariance matrix computation now much faster) Fixed bug with default marginal posterior correction when using gp_predcm Fixed conditions in likelihood functions for grid approximation of predictions with marginal posterior corrections Fixed outputs of gpmc_preds with multilatent models (thanks to Mahdi Biparva for pointing this out) and some minor bug fixes	June 14, 2013, 12:30:18
4.1	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	April 25, 2013, 11:07:06
4.0	Initial Announcement on mloss.org.	March 22, 2013, 08:14:05

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