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.
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
)a Kriging model of "km" class
a matrix containing the new points of experiments
a matrix containing the function values on the points NewX
scalar: noise variance
kriging type: "SK" or "UK"
boolean: if TRUE, the new point does not exist already in the design of experiment model@X
optional integer: if add.obs=TRUE, it specifies the index of the observation in model@X corresponding to x.new
optional boolean specfiying if the covariance parameters should be re-estimated (default value = TRUE)
optional boolean specfiying if the noise variance should be re-estimated (default value = TRUE)
optional input of "km" class. Required if NoiseReEstimate=TRUE, in order to deal with repetitions.
optional scalar: is used to define a lower bound on the noise variance.
A list containing:
The updated Kriging model
If NoiseReEstimate=TRUE, the updated estim.model
If NoiseReEstimate=TRUE, the re-estimated noise variance
V. Picheny and D. Ginsbourger (2013), Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package, Computational Statistics & Data Analysis