/// <summary>
/// Generates a sample from the <c>NormalGamma</c> distribution.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="meanLocation">The location of the mean.</param>
/// <param name="meanScale">The scale of the mean.</param>
/// <param name="precisionShape">The shape of the precision.</param>
/// <param name="precisionInverseScale">The inverse scale of the precision.</param>
/// <returns>a sample from the distribution.</returns>
public static MeanPrecisionPair Sample(Random rnd, double meanLocation, double meanScale, double precisionShape, double precisionInverseScale)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(meanLocation, meanScale, precisionShape, precisionInverseScale))
{
throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
}
var mp = new MeanPrecisionPair();
// Sample the precision.
mp.Precision = Double.IsPositiveInfinity(precisionInverseScale) ? precisionShape : Gamma.Sample(rnd, precisionShape, precisionInverseScale);
// Sample the mean.
mp.Mean = meanScale == 0.0 ? meanLocation : Normal.Sample(rnd, meanLocation, Math.Sqrt(1.0 / (meanScale * mp.Precision)));
return(mp);
}
/// <summary>
/// Generates a sequence of samples from the NormalGamma distribution
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="meanLocation">The location of the mean.</param>
/// <param name="meanScale">The scale of the mean.</param>
/// <param name="precisionShape">The shape of the precision.</param>
/// <param name="precisionInvScale">The inverse scale of the precision.</param>
/// <returns>a sequence of samples from the distribution.</returns>
public static IEnumerable <MeanPrecisionPair> Samples(System.Random rnd, double meanLocation, double meanScale, double precisionShape, double precisionInvScale)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(meanLocation, meanScale, precisionShape, precisionInvScale))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
while (true)
{
var mp = new MeanPrecisionPair();
// Sample the precision.
mp.Precision = double.IsPositiveInfinity(precisionInvScale) ? precisionShape : Gamma.Sample(rnd, precisionShape, precisionInvScale);
// Sample the mean.
mp.Mean = meanScale == 0.0 ? meanLocation : Normal.Sample(rnd, meanLocation, Math.Sqrt(1.0 / (meanScale * mp.Precision)));
yield return(mp);
}
}
/// <summary>
/// Evaluates the log probability density function for a NormalGamma distribution.
/// </summary>
/// <param name="mp">The mean/precision pair of the distribution</param>
/// <returns>The log of the density value</returns>
public double DensityLn(MeanPrecisionPair mp)
{
return(DensityLn(mp.Mean, mp.Precision));
}
/// <summary>
/// Generates a sequence of samples from the NormalGamma distribution
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="meanLocation">The location of the mean.</param>
/// <param name="meanScale">The scale of the mean.</param>
/// <param name="precisionShape">The shape of the precision.</param>
/// <param name="precisionInvScale">The inverse scale of the precision.</param>
/// <returns>a sequence of samples from the distribution.</returns>
public static IEnumerable<MeanPrecisionPair> Samples(Random rnd, double meanLocation, double meanScale, double precisionShape, double precisionInvScale)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(meanLocation, meanScale, precisionShape, precisionInvScale))
{
throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
}
while (true)
{
var mp = new MeanPrecisionPair();
// Sample the precision.
mp.Precision = Double.IsPositiveInfinity(precisionInvScale) ? precisionShape : Gamma.Sample(rnd, precisionShape, precisionInvScale);
// Sample the mean.
mp.Mean = meanScale == 0.0 ? meanLocation : Normal.Sample(rnd, meanLocation, Math.Sqrt(1.0 / (meanScale * mp.Precision)));
yield return mp;
}
}
/// <summary>
/// Evaluates the log probability density function for a NormalGamma distribution.
/// </summary>
/// <param name="mp">The mean/precision pair of the distribution</param>
/// <returns>The log of the density value</returns>
public double DensityLn(MeanPrecisionPair mp)
{
return DensityLn(mp.Mean, mp.Precision);
}
/// <summary>
/// Generates a sample from the <c>NormalGamma</c> distribution.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="meanLocation">The location of the mean.</param>
/// <param name="meanScale">The scale of the mean.</param>
/// <param name="precisionShape">The shape of the precision.</param>
/// <param name="precisionInverseScale">The inverse scale of the precision.</param>
/// <returns>a sample from the distribution.</returns>
public static MeanPrecisionPair Sample(System.Random rnd, double meanLocation, double meanScale, double precisionShape, double precisionInverseScale)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(meanLocation, meanScale, precisionShape, precisionInverseScale))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
var mp = new MeanPrecisionPair();
// Sample the precision.
mp.Precision = double.IsPositiveInfinity(precisionInverseScale) ? precisionShape : Gamma.Sample(rnd, precisionShape, precisionInverseScale);
// Sample the mean.
mp.Mean = meanScale == 0.0 ? meanLocation : Normal.Sample(rnd, meanLocation, Math.Sqrt(1.0/(meanScale*mp.Precision)));
return mp;
}
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).ToArray();
var precs = samples.Select(mp => mp.Precision).ToArray();
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the precision distribution.
CommonDistributionTests.ContinuousVapnikChervonenkisTest(
CommonDistributionTests.ErrorTolerance,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.ContinuousVapnikChervonenkisTest(
CommonDistributionTests.ErrorTolerance,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
/// <summary>
/// Generates a sample from the NormalGamma distribution.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="meanLocation">The location of the mean.</param>
/// <param name="meanScale">The scale of the mean.</param>
/// <param name="precShape">The shape of the precision.</param>
/// <param name="precInvScale">The inverse scale of the precision.</param>
/// <returns>a sample from the distribution.</returns>
public static MeanPrecisionPair Sample(System.Random rnd, double meanLocation, double meanScale, double precShape, double precInvScale)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(meanLocation, meanScale, precShape, precInvScale))
{
throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
}
MeanPrecisionPair mp = new MeanPrecisionPair();
// Sample the precision.
if(Double.IsPositiveInfinity(precInvScale))
{
mp.Precision = precShape;
}
else
{
mp.Precision = Gamma.Sample(rnd, precShape, precInvScale);
}
// Sample the mean.
if (meanScale == 0.0)
{
mp.Mean = meanLocation;
}
else
{
mp.Mean = Normal.Sample(rnd, meanLocation, System.Math.Sqrt(1.0 / (meanScale * mp.Precision)));
}
return mp;
}
