deepICMGP: Deep Intrinsic Coregionalization Gaussian Processes for Multiple
Outputs
Fits two-layer deep intrinsic coregionalization Gaussian process
surrogate models for multiple outputs observed at shared input locations.
Uses Markov chain Monte Carlo sampling for kernel parameters and latent
coordinates, with plug-in coregionalization estimates. Provides burn-in
removal and thinning, posterior predictive means and within-location output
covariances, and sequential experimental design using an active learning
criterion. The method is described by Chang and Sung (2026)
<doi:10.1615/Int.J.UncertaintyQuantification.2026062570>.
Adapts code from the 'deepgp' package, with underlying deep Gaussian process
methodology described by Sauer, Gramacy, and Higdon (2023)
<doi:10.1080/00401706.2021.2008505>.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=deepICMGP
to link to this page.