Sequential Design for Deep Gaussian Processes using MCMC
Performs model fitting and sequential design for deep Gaussian
processes following Sauer, Gramacy, and Higdon (2020) . 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.