Sequential Design for Deep Gaussian Processes using MCMC
Performs model fitting and sequential design for deep Gaussian
processes using MCMC and elliptical slice sampling.  Models extend up to
three layers deep; a one layer model is equivalent to typical Gaussian
process regression.  Sequential design criteria include integrated mean
square prediction error (IMSPE), 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 C under the hood.  Manuscript
forthcoming; see Damianou and Lawrence (2013) <arXiv:1211.0358> for deep
Gaussian process models and Murray, Adams, and MacKay (2010) <arXiv:1001.0175>
for elliptical slice sampling.
grDevices, graphics, stats, doParallel, foreach, parallel

Performs model fitting and sequential design for deep Gaussian
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 C under the hood.

Performs model fitting and sequential design for deep Gaussian
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
C under the hood.