Kriging-Based, Gaussian Cdf Estimator

Gaussian_cdf_Kestimator<covariance,kriging_constraints,matrix_lib>

This cdf estimator assumes the cdf of variable Z is Gaussian, and does not account for any secondary variable. The mean and variance of the Gaussian cdf are estimated by kriging.

Where Defined

In header file <cdf_estimators.h>

Template Parameters

covariance		is a model of Covariance
kriging_constraints		is model of Kriging Constraint. It is set by default to OK_constraints<tnt_lib>
matrix_lib		defines the library of linear algebra to be used. The default value is tnt_lib, the TNT library.

Model of

Single Variable Cdf Estimator

Members

The leading Gaussian_cdf_Kestimator:: is omitted in the list of member functions.

• Gaussian_cdf_Kestimator(const covariance& cov,
                          const kriging_constraints& Kconstraints)
  
  Constructs a Gaussian_cdf_Kestimator. It requires the covariance function: Cov$\Big($Z($\bf u$), Z($\bf u$ + $\bf h$)$\Big)$, and a set of kriging constraints (e.g. simple kriging constraints).

• template<class Location, class Neighborhood, class GaussianCdf> 
  int operator()(const Location& u, const Neighborhood& neighbors,
                 GaussianCdf& ccdf)
  
  Function call operator. It estimates the gaussian cdf parameters and modifies ccdf accordingly. location is a model of Location, and neighborhood is a model of Neighborhood.

  u is the location at which the Gaussian conditional cdf is estimated.

  neighbors is the neighborhood of location u. The function returns 0 if no problem occurred.