/// <summary>
/// Computes the log density of the Student t-distribution.
/// </summary>
/// <param name="x">The location at which to compute the log density.</param>
/// <returns>the log density at <paramref name="x"/>.</returns>
public double DensityLn(double x)
{
// TODO JVG we can probably do a better job for Cauchy special case
if (_dof >= 1e+8d)
{
return(Normal.DensityLn(_location, _scale, x));
}
var d = (x - _location) / _scale;
return(SpecialFunctions.GammaLn((_dof + 1.0) / 2.0)
- (0.5 * ((_dof + 1.0) * Math.Log(1.0 + (d * d / _dof))))
- SpecialFunctions.GammaLn(_dof / 2.0)
- (0.5 * Math.Log(_dof * Math.PI)) - Math.Log(_scale));
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Normal_distribution">Normal distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Normal distribution class with parameters Mean = 0, StdDev = 1
var normal = new Normal(0, 1);
Console.WriteLine(@"1. Initialize the new instance of the Normal distribution class with parameters Mean = {0}, StdDev = {1}", normal.Mean, normal.StdDev);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", normal);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", normal.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", normal.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", normal.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", normal.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", normal.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", normal.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", normal.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", normal.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", normal.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", normal.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", normal.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", normal.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples
Console.WriteLine(@"3. Generate 10 samples");
for (var i = 0; i < 10; i++)
{
Console.Write(normal.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Normal(0, 1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Normal(0, 1) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = normal.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Normal(-10, 0.2) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Normal(-10, 0.01) distribution and display histogram");
normal.Mean = -10;
normal.StdDev = 0.01;
for (var i = 0; i < data.Length; i++)
{
data[i] = normal.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}