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 Bug fixes - Periodic covariance function works with selectedVariables - Survival likelihoods (log-Gaussian, log-Logistic and Weibull) work now with empty z for uncensored data - Return parameters in correct order from gp_pak if using hyperhyperpriors - Other bug fixes 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 2013-10-14 Version 4.3 Improvements: - lgpdens.m: better default estimation using importance and rejection sampling, better default priors - 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 2013-06-13 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 (utilize log distributions) - 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 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 2013-03-12 Version 4.0 New features: - Multilatent models: multinomial, softmax, Cox-PH, density estimation, density regression, input dependent noise, input dependent overdispersion in Weibull, zero-inflated negative binomial - Survival models: Cox-PH, Log-Gaussian, Log-logistic, diagnostic criteria - Quantile regression - PASS-GP active set selection for classification - optional memory save in gradient computation - approximative gradient for EP-LOO - Octave compatibility. Please download Octave specific version of GPstuff to use GPstuff with Octave. Following features of v4.0 work only with Matlab: - Inputdependent multilatent models - Zero-Inflated Negative-Binomial model - Cox proportional hazard model - Compactly Supported (PPCS*) covariance functions - Kronecker speedup for density estimation Improvements: - much faster parallel-EP (now default) - faster sequential EP New functions & files - demo_inputdependentnoise: input dependent noise in Gaussian model - demo_inputdependentweibull: input dependent overdispersion in Weibull - demo_lgpdens: density estimation, density regression - demo_loopred: leave-one-out cross-validation approximations - demo_mcmc: different MCMC methods demonstrated - demo_memorysave: memory save in gradient computation - demo_modelcomparison2: additional model comparsion demo - demo_multiclass: multi-class classification - demo_multinom: multinomial model - demo_passgp: PASS-GP active set selection for classification - demo_quantilegp: Quantile regression - demo_survival_comparison: survival model diagnostic criteria - demo_survival_coxph: Gaussian process Cox-PH model - demo_zinegbin: zero-inflated negative binomial - gpep_loog.m: approximate gradient for EP-LOO - gp_kfcv_cdf.m: K-fold cross validation to predict CDF for GP model - gp_kfcve.m: mean negative log k-fold-cv predictive density. - gpla_looe.m: Laplace Leave-one-out energy (negative preditive density) - gp_predcdf.m: Predictive distribution CDF estimation - lgpdens_cum.m: Bayesian Bootstrap density estimation integration - lgpdens.m: Density estimation with Gaussian Processes - lik_coxph.m: Cox proportaional hazard likelihood - lik_inputdependentnoise.m: Input-dependent noise likelihood - lik_inputdependentweibull.m: Input-dependent Weibull likelihood - lik_lgpc.m: Logistic likelihood for conditional density estimation - lik_lgp.m: Logistic likelihood for density estimation - lik_loggaussian.m: Log-Gaussian likelihood - lik_loglogistic.m: Log-logistic likelihood - lik_multinom.m: Multinomial likelihood - lik_qgp.m: Quantile-GP regression likelihood - lik_softmax.m: Softmax (multiclass) likelihood - lik_zinegbin.m: Zero-Inflated Negative-Binomial likelihood - passgp.m: Pass-GP routine - pred_coxphhs.m: Hazard and survival functions for Cox-Ph likelihood - pred_coxph.m: Returns useful values for Cox-PH likelihood - pred_coxphp.m: Integrate model (cox-ph) with respect to time And some bug fixes 2012-10-29 Version 3.4.1 published. Bug fixes - LOO-CV predictions fixed (gp_loopred, gpmc_loopred, gpla_e, gpep_e) - k-fold-cv for PIC sparse approximation fixed (gp_kfcv) - other bug fixes (gp_pred, gp_e, prior_t) Improved functions - LOO-CV added to demo_modelassessment1, demo_modelassessment2, xunit tests - DTC,VAR,SOR sparse approximations for Laplace (gpla_g, gpla_pred) - improved robustnes of optimisation functions (fminscg, fminlbfgs) - new function for setting random stream (setrandstream) - many demos made to display less clutter 2012-10-08 Version 3.4 published. Improved functions - GP_IA improved autoscale and display options - GP_KFCV optional return values for cvpreds (f,lp,y) - GP_LOOEG approximate gradients for EP-LOO (no implicit terms) - GP_OPTIM new options 'lambda' and 'lambdalimit' used by fminscg - GP_SET new option 'savememory' for memory saving in gradient calculations - FMINSCG new options 'lambda' and 'lambdalimit' Other changes - SuiteSparse v 3.4 included in the distribution package - gp/demos renamed to gp/demodata - Few bug fixes and several documentation fixes 2012-06-20 Version 3.3 published. Some new functions and bug fixes. New functions - GP_CPRED Conditional predictions using specific covariates - GPCF_SCALED Create a scaled covariance function - SURROGATE_SLS Markov chain Monte Carlo sampling using Surrogate data Slice Sampling - PRIOR_INVT Inverse Student-t prior structure - PRIOR_INVUNIF Inverse uniform prior structure - PRIOR_LOGT Student-t prior structure for the logarithm of the parameter - HMC_NUTS No-U-Turn Sampler (NUTS) - ADDLOGS Add numbers represented by their logarithms. - LOGITINV Inverse of the logit transformation - MAPCOLOR Returns a colormap ranging from blue through gray to red - SUMLOGS Sum of vector where numbers are represented by their logarithms. - TEST_ALL Unit testing with xunit package (requires xunit from Mathowrks File Exchange) Improved functions - Robust-EP method improved - GP_PRED Makes predictions using training data if no test data is given - GP_LOOPRED Supports now sparse approximations FIC, PIC, CS+FIC and latent method Laplace - GP_MC By default use Surrogate Slice Sampler for hyperparameters and Elliptical Slice Sampler for latent values. Supports now also NUTS. - SLS Added Shrinking-rank SLS, Covariance-matching SLS Several bug and documentation fixes 2012-03-16 Version 3.2.1 published. Many bug fixes. 2011-10-11 Version 3.2 published. New observation models and new inference algorithms (e.g. EP for Student-t model) added plus bug fixes. 2011-04-15 Version 3.1 published. New functionalities and major update to argument syntax making the package easier to use.