edm_crit: Distance in measure criterion

In pGPx: Pseudo-Realizations for Gaussian Process Excursions

Distance in measure criterion

Description

Computes the distance in measure criterion.

Usage

edm_crit(
  x,
  integration.points,
  integration.weights = NULL,
  intpoints.oldmean,
  intpoints.oldsd,
  precalc.data,
  model,
  threshold,
  batchsize,
  alpha,
  current.crit
)

Arguments

x	vector of dimension d representing the ith point where to compute the criterion
integration.points	p*d matrix of points for numerical integration in the X space.
integration.weights	Vector of size p corresponding to the weights of these integration points.
intpoints.oldmean	Vector of size p corresponding to the kriging mean at the integration points.
intpoints.oldsd	Vector of size p corresponding to the kriging standard deviation at the integration points.
precalc.data	list result of precomputeUpdateData with model and x.
model	km model
threshold	threshold selected for excursion set
batchsize	number of simulation points
alpha	value of Vorob'ev threshold
current.crit	Current value of the criterion

Value

the value of the expected distance in measure criterion at x

References

Azzimonti D. F., Bect J., Chevalier C. and Ginsbourger D. (2016). Quantifying uncertainties on excursion sets under a Gaussian random field prior. SIAM/ASA Journal on Uncertainty Quantification, 4(1):850–874.

Azzimonti, D. (2016). Contributions to Bayesian set estimation relying on random field priors. PhD thesis, University of Bern.

pGPx documentation built on Aug. 23, 2023, 5:09 p.m.