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. 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 utilizes C and C++ under the hood.


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install.packages("deepgp")

0.3.0 by Annie Sauer, 4 days ago


Browse source code at https://github.com/cran/deepgp


Authors: Annie Sauer <[email protected]>


Documentation:   PDF Manual  


LGPL license


Imports grDevices, graphics, stats, doParallel, foreach, parallel, Rcpp, mvtnorm

Suggests akima, knitr

Linking to Rcpp, RcppArmadillo, BH


See at CRAN