public void Apply_Expectation_matches_AttractorPoint_for_large_times()
{
var tolerance = 1e-7;
var time = 1e50;
var process = new OrnsteinUhlenbeckProcess(_diffusionMatrix, _relaxationTime, _attractorPoint);
Expect(((OVector)process.Apply(_point, time).Expectation - _attractorPoint).Norm(), Is.LessThan(tolerance));
}
public void Apply_Covariance_matches_EquilibriumCovariance_for_large_times()
{
var tolerance = 1e-7;
var time = 1e50;
var process = new OrnsteinUhlenbeckProcess(_diffusionMatrix, _relaxationTime, _attractorPoint);
Expect((process.Apply(_point, time).Covariance - process.EquilibriumCovariance).FrobeniusNorm(), Is.LessThan(tolerance));
}
public void EquilibriumCovariance_of_OrnsteinUhlenbeckProcess_GivenByCovariance_matches_provided_covariance()
{
var covariance = new SymmetricMatrix(new double[][]
{
new double [] { +0.3, },
new double [] { -0.1, +0.4, },
new double [] { +0.1, +0.2, +0.4 }
});
var process = OrnsteinUhlenbeckProcess.GivenByCovariance(covariance, _relaxationTime, _attractorPoint);
Expect((process.EquilibriumCovariance - covariance).FrobeniusNorm(), Is.LessThan(_tolerance));
}
public void Common_IStochasticProcess_validation()
{
var process = new OrnsteinUhlenbeckProcess(_diffusionMatrix, _relaxationTime, _attractorPoint);
ProbabilityTestUtils.ValidateProcess(process, _point, time: 7.4, eps: 1e-5, tolerance: 1e-7);
}
