#include <ControlledRandomVariable.h>
Inheritance diagram for ControlledRandomVariable:
Definition at line 52 of file ControlledRandomVariable.h.
Public Member Functions  
Real  getBeta () const 
void  setBeta () 
ControlledRandomVariable ()  
virtual RealVector  nextValue ()=0 
virtual Real  getControlVariateMean ()=0 
RandomVariable *  controlled () 
Real  expectation (int N) 
Real  expectation (int N, string message) 
void  controlVariateMeanTest (int N) 
Real  betaCoefficient () 
Real  correlationWithControlVariate (int N) 



Definition at line 63 of file ControlledRandomVariable.h. References Real. 

This function MUST be called from the concrete subclass defining Definition at line 71 of file ControlledRandomVariable.h. References betaCoefficient(). Referenced by StandardNormalVariable::StandardNormalVariable(). 

random deviate  control variate pair from base class RandomVector so a derived class can define this. Implements RandomObject< RealVector >. Implemented in StandardNormalVariable. 

The mean of the control variate. It is either known or derived from a simulation which is significantly faster than the simulation of X ( Implemented in StandardNormalVariable. 

The random variable , where Y is the control variate of X and is the beta coefficient. 

Expectation of X computed from a sample of size N. It's simply the ordinary Monte Carlo expectation of the controlled version
Reimplemented from RandomObject< RealVector >. 

Same as expectation(int) but with computational progress reported as count down.
Reimplemented from RandomObject< RealVector >. 

Tests if the method for computing the mean of the control variate at time zero is correct by comparing the returned value against a Monte Carlo mean of the control variate.


Computes the coefficient beta=Cov(X,Y)/Var(X), where Y is the control variate of X ( Referenced by setBeta(). 

The correlation of the control variate with random variable X ( This routine is only called to test the quality of a prospective control variate. Thus we forgo efficiency and simply reduce this to variance, covariance computations.
