@Test public void testMean() { System.out.println("var"); GammaDistribution instance = new GammaDistribution(3, 2.1); instance.rand(); assertEquals(6.3, instance.mean(), 1E-7); }
@Override public double mean() { return k * theta; }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double mean() { return k * theta; } }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double mean() { return k * theta; } GammaDistribution(double shape, double scale); }
GammaDistribution extends AbstractDistribution implements ExponentialFamily { @Override public double mean() { return k * 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 mean() { return k * 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; }