public void SamplesFollowsCorrectDistribution()
{
var cd = new NormalGamma(1.0, 4.0, 3.0, 3.5);
// Sample from the distribution.
var samples = cd.Samples().Take(CommonDistributionTests.NumberOfTestSamples).ToArray();
// Extract the mean and precisions.
var means = samples.Select(mp => mp.Mean);
var precs = samples.Select(mp => mp.Precision);
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the precision distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
public void SampleFollowsCorrectDistribution()
{
var cd = new NormalGamma(1.0, 4.0, 7.0, 3.5);
// Sample from the distribution.
var samples = new MeanPrecisionPair[CommonDistributionTests.NumberOfTestSamples];
for (var i = 0; i < CommonDistributionTests.NumberOfTestSamples; i++)
{
samples[i] = cd.Sample();
}
// Extract the mean and precisions.
var means = samples.Select(mp => mp.Mean);
var precs = samples.Select(mp => mp.Precision);
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the precision distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
