public void InverseGaussianSummation()
{
// X_i ~ IG(\mu,\lambda) \rightarrow \sum_{i=0}^{n} X_i ~ IG(n \mu, n^2 \lambda)
Random rng = new Random(1);
WaldDistribution d0 = new WaldDistribution(1.0, 2.0);
List <double> s = new List <double>();
for (int i = 0; i < 64; i++)
{
s.Add(d0.GetRandomValue(rng) + d0.GetRandomValue(rng) + d0.GetRandomValue(rng));
}
WaldDistribution d1 = new WaldDistribution(3.0 * 1.0, 9.0 * 2.0);
TestResult r = s.KolmogorovSmirnovTest(d1);
Assert.IsTrue(r.Probability > 0.05);
}
public void InverseGaussianSummation()
{
// X_i ~ IG(\mu,\lambda) \rightarrow \sum_{i=0}^{n} X_i ~ IG(n \mu, n^2 \lambda)
Random rng = new Random(0);
WaldDistribution d0 = new WaldDistribution(1.0, 2.0);
Sample s = new Sample();
for (int i = 0; i < 64; i++)
{
s.Add(d0.GetRandomValue(rng) + d0.GetRandomValue(rng) + d0.GetRandomValue(rng));
}
WaldDistribution d1 = new WaldDistribution(3.0 * 1.0, 9.0 * 2.0);
TestResult r = s.KolmogorovSmirnovTest(d1);
Console.WriteLine(r.LeftProbability);
}