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hetGP (version 1.1.6)

Heteroskedastic Gaussian Process Modeling and Design under Replication

Description

Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) , with implementation details in Binois, M. & Gramacy, R. B. (2021) . 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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Version

Install

install.packages('hetGP')

Monthly Downloads

690

Version

1.1.6

License

LGPL

Maintainer

Mickael Binois

Last Published

October 2nd, 2023

Functions in hetGP (1.1.6)

Parallel Expected improvement

Integrated Contour Uncertainty criterion

t-MSE criterion

Sequential IMSPE criterion

Maximum Empirical Error criterion

Contour Stepwise Uncertainty Reduction criterion

Maximum Contour Uncertainty criterion

Correlation function of selected type, supporting both isotropic and product forms

Criterion optimization

Expected Improvement criterion

Noisy 1d test function (2) Add Gaussian noise with variance r(x) = scale * (exp(sin(2 pi x)))^2 to f1d2

Data preprocessing

Noisy 1d test function (1) Add Gaussian noise with variance r(x) = scale * (1.1 + sin(2 pi x))^2 to f1d

1d test function (1)

1d test function (2)

Generic Log-likelihood function This function can be used to compute loglikelihood for homGP/hetGP models

Derivative of EI criterion for GP models

deriv_crit_IMSPE

Derivative of crit_IMSPE

Gaussian process modeling with heteroskedastic noise

Gaussian process predictions using a GP object for correlated noise (of class CRNGP)

pred_noisy_input

Gaussian process prediction prediction at a noisy input x, with centered Gaussian noise of variance sigma_x. Several options are available, with different efficiency/accuracy tradeoffs.

Gaussian process modeling with homoskedastic noise

Student-t process predictions using a heterogeneous noise TP object (of class hetTP)

Gaussian process modeling with correlated noise

Student-T process modeling with homoskedastic noise

Gaussian process predictions using a heterogeneous noise GP object (of class hetGP)

Gaussian process predictions using a homoskedastic noise GP object (of class homGP)

Student-t process modeling with heteroskedastic noise

Score and RMSE function To asses the performance of the prediction, this function computes the root mean squared error and proper score function (also known as negative log-probability density).

Fast homGP-update

Update "hetTP"-class model fit with new observations

Fast conditional simulation for a CRNGP model

Conditional simulation for CRNGP

Update "hetGP"-class model fit with new observations

Fast homTP-update

Student-t process predictions using a homoskedastic noise GP object (of class homGP)

Compute double integral of the covariance kernel over a [0,1]^d domain

Import and export of hetGP objects

IMSPE optimization

Leave one out predictions

Likelihood-based comparison of models

Bayes Factor Data

Allocation of replicates on existing designs

Integrated Mean Square Prediction Error

SIR test problem

Assemble To Order (ATO) Data and Fits