public void SampleTest()
{
double Mean = 5 * rnd.NextDouble();
double Sdv = 5 * rnd.NextDouble();
Normal dis = new Normal(Mean, Sdv);
UnivariateHybridMC Hybrid = new UnivariateHybridMC(0, dis.DensityLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
double[] Sample = Hybrid.Sample(10000);
double Effective = MCMCDiagnostics.EffectiveSize(Sample, x => x);
DescriptiveStatistics Stats = new DescriptiveStatistics(Sample);
//Testing the mean using the normal distribution.
double MeanConvergence = 3 * Sdv / Math.Sqrt(Effective);
Assert.AreEqual(Stats.Mean, Mean, MeanConvergence, "Mean");
double DeviationRation = Math.Pow(Stats.StandardDeviation / Sdv, 2);
//Approximating chi-square with normal distribution. (Degree of freedom is large)
double DeviationConvergence = 3 * Math.Sqrt(2) / Math.Sqrt(Effective);
Assert.AreEqual(DeviationRation, 1, DeviationConvergence, "Standard Deivation");
}
public void SampleTest(double mean, double sdv, int seed)
{
var dis = new Normal(mean, sdv);
var hybrid = new UnivariateHybridMC(0, dis.DensityLn, 10, 0.1, 1000, 1, new System.Random(seed))
{
BurnInterval = 0
};
double[] sample = hybrid.Sample(10000);
double effective = MCMCDiagnostics.EffectiveSize(sample, x => x);
var stats = new DescriptiveStatistics(sample);
//Testing the mean using the normal distribution.
double meanConvergence = 3 * sdv / Math.Sqrt(effective);
Assert.AreEqual(stats.Mean, mean, meanConvergence, "Mean");
double deviationRation = Math.Pow(stats.StandardDeviation / sdv, 2);
//Approximating chi-square with normal distribution. (Degree of freedom is large)
double deviationConvergence = 3 * Math.Sqrt(2) / Math.Sqrt(effective);
Assert.AreEqual(deviationRation, 1, deviationConvergence, "Standard Deivation");
}
public void UnivariateHybridMCConstructor()
{
var hybrid = new UnivariateHybridMC(0, _normal.DensityLn, 10, 0.1);
Assert.IsNotNull(hybrid.RandomSource);
hybrid.RandomSource = new System.Random();
Assert.IsNotNull(hybrid.RandomSource);
}
public void UnivariateHybridMCConstructor()
{
UnivariateHybridMC Hybrid = new UnivariateHybridMC(0, normal.DensityLn, 10, 0.1);
Assert.IsNotNull(Hybrid.RandomSource);
Hybrid.RandomSource = new System.Random();
Assert.IsNotNull(Hybrid.RandomSource);
}
public void SampleTest()
{
double Mean = 5 * rnd.NextDouble();
double Sdv = 5 * rnd.NextDouble();
Normal dis = new Normal(Mean, Sdv);
UnivariateHybridMC Hybrid = new UnivariateHybridMC(0, dis.DensityLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
double[] Sample = Hybrid.Sample(10000);
double Effective = MCMCDiagnostics.EffectiveSize(Sample,x=>x);
DescriptiveStatistics Stats = new DescriptiveStatistics(Sample);
//Testing the mean using the normal distribution.
double MeanConvergence = 3 * Sdv / Math.Sqrt(Effective);
Assert.AreEqual(Stats.Mean, Mean, MeanConvergence, "Mean");
double DeviationRation = Math.Pow(Stats.StandardDeviation / Sdv, 2);
//Approximating chi-square with normal distribution. (Degree of freedom is large)
double DeviationConvergence = 3 * Math.Sqrt(2) / Math.Sqrt(Effective);
Assert.AreEqual(DeviationRation, 1, DeviationConvergence, "Standard Deivation");
}
public void SampleTest(double mean, double sdv, int seed)
{
var dis = new Normal(mean, sdv);
var hybrid = new UnivariateHybridMC(0, dis.DensityLn, 10, 0.1, 1000, 1, new System.Random(seed))
{
BurnInterval = 0
};
double[] sample = hybrid.Sample(10000);
double effective = MCMCDiagnostics.EffectiveSize(sample, x => x);
var stats = new DescriptiveStatistics(sample);
//Testing the mean using the normal distribution.
double meanConvergence = 3*sdv/Math.Sqrt(effective);
Assert.AreEqual(stats.Mean, mean, meanConvergence, "Mean");
double deviationRation = Math.Pow(stats.StandardDeviation/sdv, 2);
//Approximating chi-square with normal distribution. (Degree of freedom is large)
double deviationConvergence = 3*Constants.Sqrt2/Math.Sqrt(effective);
Assert.AreEqual(deviationRation, 1, deviationConvergence, "Standard Deviation");
}
