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hetGP (version 1.1.2)

Heteroskedastic Gaussian Process Modeling and Design under Replication

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.

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Version

Install

install.packages('hetGP')

Monthly Downloads

659

Version

1.1.2

License

LGPL

Maintainer

Mickael Binois

Last Published

October 24th, 2019

Functions in hetGP (1.1.2)

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

Allocation of replicates on existing designs

Import and export of hetGP objects

Bayes Factor Data

Assemble To Order (ATO) Data and Fits

SIR test problem

IMSPE optimization

Likelihood-based comparison of models

Integrated Mean Square Prediction Error

Leave one out predictions

t-MSE criterion

Maximum Empirical Error criterion

Maximum Contour Uncertainty criterion

Integrated Contour Uncertainty criterion

Sequential IMSPE criterion

Criterion optimization

Contour Stepwise Uncertainty Reduction criterion

1d test function (1)

Expected Improvement criterion

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

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

Derivative of EI criterion for GP models

deriv_crit_IMSPE

Derivative of crit_IMSPE

1d test function (2)

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

Student-T process modeling with homoskedastic noise

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

Data preprocessing

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

Update "hetGP"-class model fit with new observations

Update "hetTP"-class model fit with new observations

Gaussian process modeling with homoskedastic noise

Student-t process modeling with heteroskedastic noise

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

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

Gaussian process modeling with heteroskedastic noise

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

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

Fast homTP-update