@Test public void testVariance() { System.out.println("variance"); GammaDistribution instance = new GammaDistribution(3, 2.1); instance.rand(); assertEquals(13.23, instance.variance(), 1E-7); }
@Override public double variance() { return k * theta * theta; }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double variance() { return k * theta * theta; } }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double variance() { return k * theta * theta; } GammaDistribution(double shape, double scale); }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double variance() { return k * theta * theta; } GammaDistribution(double shape, double scale); static GammaDistribution fit(double[] data); @Override int length(); @Override double mean(); @Override double variance(); @Override double sd(); @Override double entropy(); @Override String toString(); @Override double rand(); @Override double p(double x); @Override double logp(double x); @Override double cdf(double x); @Override double quantile(double p); @Override Mixture.Component M(double[] x, double[] posteriori); }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double variance() { return k * theta * theta; } GammaDistribution(double shape, double scale); static GammaDistribution fit(double[] data); @Override int length(); @Override double mean(); @Override double variance(); @Override double sd(); @Override double entropy(); @Override String toString(); @Override double rand(); @Override double p(double x); @Override double logp(double x); @Override double cdf(double x); @Override double quantile(double p); @Override Mixture.Component M(double[] x, double[] posteriori); final double theta; final double k; }