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.
2015-07-09 Version 4.6
Development and release branches available at https://github.com/gpstuff-dev/gpstuff
Use Pareto smoothed importance sampling (Vehtari & Gelman, 2015) for
importance sampling leave-one-out cross-validation
importance sampling integration over hyperparameters
importance sampling part of the logistic Gaussian process density
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
- faster EP moment calculation for lik_logit
Several minor bugfixes