Maintainer: Annie S. Booth annie_booth@ncsu.edu
Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <arXiv:2012.08015>). See Sauer (2023, http://hdl.handle.net/10919/114845) for comprehensive methodological details and https://bitbucket.org/gramacylab/deepgp-ex/ for a variety of coding examples. 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, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>), and contour location through entropy (Sauer, 2023). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.
Run help("deepgp-package")
or
help(package = "deepgp")
for more information.
Sauer, A. (2023). Deep Gaussian process surrogates for computer experiments. Ph.D. Dissertation, Department of Statistics, Virginia Polytechnic Institute and State University.
Sauer, A., Gramacy, R.B., & Higdon, D. (2023). Active learning for deep Gaussian process surrogates. Technometrics, 65, 4-18. arXiv:2012.08015
Sauer, A., Cooper, A., & Gramacy, R. B. (2022). Vecchia-approximated deep Gaussian processes for computer experiments. Journal of Computational and Graphical Statistics, 1-14. arXiv:2204.02904
Gramacy, R. B., Sauer, A. & Wycoff, N. (2022). Triangulation candidates for Bayesian optimization. Advances in Neural Information Processing Systems (NeurIPS), 35, 35933-35945. arXiv:2112.07457
What’s new in version 1.1.1?
entropy_limit
in
any of the predict
functions.return_all = TRUE
.predict
functions no
longer return s2_smooth
or Sigma_smooth
. If
desired, these quantities may be calculated by subtracting
tau2*g
from the diagonal.vecchia = TRUE
option may now utilize either the
Matern (cov = "matern"
) or squared exponential kernel
(cov = "exp2"
“).cores = 1
in
predict
, ALC
, and IMSE
functions
(helps to avoid a SNOW conflict when running multiple instances on the
same machine).fit_two_layer
, the intermediate latent layer may now
have either a prior mean of zero (default) or a prior mean equal to
x
(pmx = TRUE
). If pmx
is set to
a constant, this will be the scale parameter on the inner Gaussian
layer.What’s new in version 1.1.0?
sep = TRUE
in
fit_one_layer
to fit a GP with separable/anisotropic
lengthscales.What’s new in version 1.0.1?
What’s new in version 1.0.0?
vecchia = TRUE
in fit functions) for
faster computation. The speed of this implementation relies on OpenMP
parallelization (make sure the -fopenmp
flag is present
with package installation).tau2
is now calculated at the time of MCMC, not at the
time of prediction. This avoids some extra calculations.What’s new in version 0.3.0?
v = 0.5
,
v = 1.5
, or v = 2.5
(default). The squared
exponential kernel is still required for use with ALC and IMSE (set
cov = "exp2"
in fit functions).EI = TRUE
inside predict
calls. EI calculations are nugget-free and are for minimizing
the response (negate y
if maximization is desired).store_latent = TRUE
inside predict.