New release GPstuff v4.0

[Aki Vehtari]

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