processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.  Models
extend up to three layers deep; a one layer model is equivalent to typical Gaussian
process regression.  Sequential design criteria include integrated mean-squared
error (IMSE), active learning Cohn (ALC), and expected
Incorporates SNOW parallelization and utilizes C under the hood.
Packaged: 2020-12-16 16:00:10 UTC; anniesauer

processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.
Models extend up to three layers deep; a one layer model is equivalent to
typical Gaussian process regression.  Sequential design criteria include
integrated mean-squared error (IMSE), active learning Cohn (ALC), and
expected improvement (EI).  Covariance structure is based on inverse
exponentiated squared euclidean distance.  Applicable to noisy and
deterministic functions.  Incorporates SNOW parallelization and utilizes
Packaged: 2021-07-15 15:20:51 UTC; anniesauer

Models extend up to three layers deep; a one layer model is equivalent
to typical Gaussian process regression.  Covariance kernel options are
Matern (default) and squared exponential.  Sequential design criteria
include integrated mean-squared error (IMSE), active learning Cohn (ALC),
and expected improvement (EI).  Applicable to both noisy and
deterministic functions.  Incorporates SNOW parallelization and
Imports: grDevices, graphics, stats, doParallel, foreach, parallel,

Packaged: 2021-12-06 18:12:56 UTC; anniesauer

Title: Deep Gaussian Processes using MCMC
Description: Performs posterior inference for deep Gaussian processes following
Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.  Models are trained through
MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings
sampling of kernel hyperparameters.  Vecchia-approximation for faster computation is implemented
following Sauer, Cooper, and Gramacy (2022) <arXiv:2204.02904>.  Downstream tasks include
sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer,
Gramacy, and Higdon, 2020) and optimization through expected improvement (EI;
Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>).  Models
process regression.  Covariance kernel options are matern (default) and squared
exponential.  Applicable to both noisy and deterministic functions.
Incorporates SNOW parallelization and utilizes C and C++ under the hood.
Packaged: 2022-04-08 13:17:27 UTC; anniesauer