About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

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

About: This toolbox implements models for Bayesian mixed-effects inference on classification performance in hierarchical classification analyses.

In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.