public double estimateArtaCorrelation(double arAutocorrelation)
{
Double result = estimationsCache[arAutocorrelation];
if (result == 0.0)
{
double e = Integrate(-8, 8, arAutocorrelation);
double mean = distribution.getNumericalMean();
double variance = distribution.getNumericalVariance();
result = (e - mean * mean) / variance;
estimationsCache.Add(arAutocorrelation, result);
}
return(result);
}
private double CalculateMinimumFeasibleBivariateCorrelation(RealDistribution distribution)
{
double minFBC = 0.0;
// consider special cases, final else is general approach
if (distribution is NormalDistribution || distribution is UniformRealDistribution || distribution is TDistribution)
{
minFBC = -1.0;
}
else if (distribution is ExponentialDistribution)
{
minFBC = 1.0 - Math.PI * Math.PI / 6.0;
}
else
{
double mean = distribution.getNumericalMean();
double variance = distribution.getNumericalVariance();
double integral = integrator.integrate(IterativeLegendreGaussIntegrator.DEFAULT_MAX_ITERATIONS_COUNT, new UnivariatDistributionFunction(distribution), 0.0, 1.0);
minFBC = (integral - mean * mean) / variance;
}
return(minFBC);
}