@Test public void testChisquareCdf() { logger.info("ChisquareCdf"); double x = 3.0; int df = 10; double expResult = 0.018575928421771; double result = ContinuousDistributions.chisquareCdf(x, df); assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH); }
public static double chisquareCdf(double x, int df) { if(df<=0) { throw new IllegalArgumentException("The degrees of freedom need to be positive."); } return ContinuousDistributions.gammaCdf(x/2.0, df/2.0); }
ContinuousDistributions { public static double chisquareCdf(double x, int df) { if(df<=0) { throw new IllegalArgumentException("The degrees of freedom need to be positive."); } return ContinuousDistributions.gammaCdf(x/2.0, df/2.0); } }
ContinuousDistributions { public static double chisquareCdf(double x, int df) { if(df<=0) { throw new IllegalArgumentException("The degrees of freedom need to be positive."); } return ContinuousDistributions.gammaCdf(x/2.0, df/2.0); }  }
ContinuousDistributions { public static double chisquareCdf(double x, int df) { if(df<=0) { throw new IllegalArgumentException("The degrees of freedom need to be positive."); } return ContinuousDistributions.gammaCdf(x/2.0, df/2.0); }  static double chisquareCdf(double x, int df); static double gaussCdf(double z); static double gamma(double x); static double logGamma(double Z); static double studentsCdf(double x, int df); static double exponentialCdf(double x, double lamda); static double betaCdf(double x, double a, double b); static double fCdf(double x, int f1, int f2); static double gammaCdf(double x, double a, double b); static double uniformCdf(double x, double a, double b); static double kolmogorov(double z); static double gaussInverseCdf(double p); static double chisquareInverseCdf(double p, int df); static double normalQuantile(double p, double mu, double sigma); static double dirichletPdf(double[] pi, double[] ai); static double dirichletPdf(double[] pi, double a); static double[] multinomialGaussianSample(double[] mean, double[][] covariance); static double multinomialGaussianPdf(double[] mean, double[][] covariance, double[] x); }
ContinuousDistributions { public static double chisquareCdf(double x, int df) { if(df<=0) { throw new IllegalArgumentException("The degrees of freedom need to be positive."); } return ContinuousDistributions.gammaCdf(x/2.0, df/2.0); }  static double chisquareCdf(double x, int df); static double gaussCdf(double z); static double gamma(double x); static double logGamma(double Z); static double studentsCdf(double x, int df); static double exponentialCdf(double x, double lamda); static double betaCdf(double x, double a, double b); static double fCdf(double x, int f1, int f2); static double gammaCdf(double x, double a, double b); static double uniformCdf(double x, double a, double b); static double kolmogorov(double z); static double gaussInverseCdf(double p); static double chisquareInverseCdf(double p, int df); static double normalQuantile(double p, double mu, double sigma); static double dirichletPdf(double[] pi, double[] ai); static double dirichletPdf(double[] pi, double a); static double[] multinomialGaussianSample(double[] mean, double[][] covariance); static double multinomialGaussianPdf(double[] mean, double[][] covariance, double[] x);  }