hetGP

Heteroskedastic Gaussian Process Modeling and Design under Replication

Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

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Description file content

Package
hetGP
Type
Package
Title
Heteroskedastic Gaussian Process Modeling and Design under Replication
Version
1.1.1
Date
2019-01-09
Author
Mickael Binois, Robert B. Gramacy
Maintainer
Mickael Binois
Description
Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) . The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
License
LGPL
LazyData
TRUE
Depends
R (>= 2.10),
Imports
Rcpp (>= 0.12.3), MASS, methods, DiceDesign
LinkingTo
Rcpp
Suggests
knitr, monomvn, lhs
VignetteBuilder
knitr
RoxygenNote
6.1.1
NeedsCompilation
yes
Packaged
2019-01-09 21:40:23 UTC; mickael
Repository
CRAN
Date/Publication
2019-01-10 14:00:16 UTC

install.packages('hetGP')

1.1.1

11 days ago

Mickael Binois

LGPL

Depends on

R (>= 2.10),

Imports

Rcpp (>= 0.12.3), MASS, methods, DiceDesign

Suggests

knitr, monomvn, lhs

Discussions