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);
}
public void CanGetMeanMarginal([Values(0.0, 10.0, 10.0)] double meanLocation, [Values(1.0, 1.0, 1.0)] double meanScale, [Values(1.0, 2.0, 2.0)] double precShape, [Values(1.0, 2.0, Double.PositiveInfinity)] double precInvScale, [Values(0.0, 10.0, 10.0)] double meanMarginalMean, [Values(1.0, 1.0, 0.5)] double meanMarginalScale, [Values(2.0, 4.0, Double.PositiveInfinity)] double meanMarginalDoF)
{
var ng = new NormalGamma(meanLocation, meanScale, precShape, precInvScale);
var mm = ng.MeanMarginal();
Assert.AreEqual(meanMarginalMean, mm.Location);
Assert.AreEqual(meanMarginalScale, mm.Scale);
Assert.AreEqual(meanMarginalDoF, mm.DegreesOfFreedom);
}
public void CanGetMeanMarginal(double meanLocation, double meanScale, double precShape, double precInvScale, double meanMarginalMean, double meanMarginalScale, double meanMarginalDoF)
{
var ng = new NormalGamma(meanLocation, meanScale, precShape, precInvScale);
var mm = ng.MeanMarginal();
Assert.AreEqual(meanMarginalMean, mm.Location);
Assert.AreEqual(meanMarginalScale, mm.Scale);
Assert.AreEqual(meanMarginalDoF, mm.DegreesOfFreedom);
}
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);
}
public void CanGetMeanMarginal(double meanLocation, double meanScale, double precShape, double precInvScale, double meanMarginalMean, double meanMarginalScale, double meanMarginalDoF)
{
var ng = new NormalGamma(meanLocation, meanScale, precShape, precInvScale);
var mm = ng.MeanMarginal();
Assert.AreEqual(meanMarginalMean, mm.Location);
Assert.AreEqual(meanMarginalScale, mm.Scale);
Assert.AreEqual(meanMarginalDoF, mm.DegreesOfFreedom);
}
public void CanGetMeanMarginal([Values(0.0, 10.0, 10.0)] double meanLocation, [Values(1.0, 1.0, 1.0)] double meanScale, [Values(1.0, 2.0, 2.0)] double precShape, [Values(1.0, 2.0, Double.PositiveInfinity)] double precInvScale, [Values(0.0, 10.0, 10.0)] double meanMarginalMean, [Values(1.0, 1.0, 0.5)] double meanMarginalScale, [Values(2.0, 4.0, Double.PositiveInfinity)] double meanMarginalDoF)
{
var ng = new NormalGamma(meanLocation, meanScale, precShape, precInvScale);
var mm = ng.MeanMarginal();
Assert.AreEqual(meanMarginalMean, mm.Location);
Assert.AreEqual(meanMarginalScale, mm.Scale);
Assert.AreEqual(meanMarginalDoF, mm.DegreesOfFreedom);
}