update_km_noisyEGO: Update of one or two Kriging models when adding new observation

Description

Update of a noisy Kriging model when adding new observation, with or without covariance parameter re-estimation. When the noise level is unkown, a twin model "estim.model" is also updated.

Usage

update_km_noisyEGO(model, x.new, y.new, noise.var = 0, type = "UK", add.obs = TRUE, index.in.DOE = NULL, CovReEstimate = TRUE, NoiseReEstimate = FALSE, estim.model = NULL, nugget.LB = 1e-05)

Arguments

model

a Kriging model of "km" class

x.new

a matrix containing the new points of experiments

y.new

a matrix containing the function values on the points NewX

noise.var

scalar: noise variance

type

kriging type: "SK" or "UK"

add.obs

boolean: if TRUE, the new point does not exist already in the design of experiment model@X

index.in.DOE

optional integer: if add.obs=TRUE, it specifies the index of the observation in model@X corresponding to x.new

CovReEstimate

optional boolean specfiying if the covariance parameters should be re-estimated (default value = TRUE)

NoiseReEstimate

optional boolean specfiying if the noise variance should be re-estimated (default value = TRUE)

estim.model

optional input of "km" class. Required if NoiseReEstimate=TRUE, in order to deal with repetitions.

nugget.LB

optional scalar: is used to define a lower bound on the noise variance.

Value

A list containing: A list containing:

References

V. Picheny and D. Ginsbourger (2013), Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package, Computational Statistics & Data Analysis