Software

dgpsi

dgpsi provides both Python and R implementations for deep and linked Gaussian process emulations. It currently has following features:

• Gaussian process emulations with separable or non-separable squared exponential and Matérn-2.5 kernels.
• Deep Gaussian process emulations with flexible structures including:
  • multiple layers;
  • multiple GP nodes;
  • separable or non-separable squared exponential and Matérn-2.5 kernels;
  • global input connections;
  • non-Gaussian likelihoods (Poisson, Negative-Binomial, heteroskedastic Gaussian, and Categorical).
• Linked emulations of feed-forward systems of computer models by linking (D)GP emulators of deterministic individual computer models.
• Fast Leave-One-Out (LOO) and Out-Of-Sample (OOS) validations for GP, DGP, and linked (D)GP emulators.
• Multi-core predictions and validations for GP, DGP, and Linked (D)GP emulators.
• Sequential designs for (D)GP emulators and bundles of (D)GP emulators.
• Automatic pruning of DGP emulators, both statically and dynamically.
• Large-scale GP, DGP, and Linked (D)GP emulations.
• Scalable DGP classification using Stochastic Imputation.
• Bayesian optimization.