scores

<code>homGP</code> or <code>hetGP</code> model, including inverse matrices

model

Xtest

corresponding vector of observations, or alternatively, 
a matrix of size [nrow(Xtest) x number of replicates], a list of size nrow(Xtest) with a least one value per element

Ztest

if <code>TRUE</code>, return the root mean squared error

return.rmse

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).

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.

Mickael Binois

hetGP

Heteroskedastic Gaussian Process Modeling and Design under
Replication

scores function

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). — scores

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).

scores: 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).

Description

Usage

Arguments

References