@Test public void testExponentialCdf() { logger.info("ExponentialCdf"); double x = 3.0; double lamda = 10.0; double expResult = 0.99999999999991; double result = ContinuousDistributions.exponentialCdf(x, lamda); assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH); }
public static double exponentialCdf(double x, double lamda) { if(x<0 || lamda<=0) { throw new IllegalArgumentException("All the parameters must be positive."); } double probability = 1.0 - Math.exp(-lamda*x); return probability; }
ContinuousDistributions { public static double exponentialCdf(double x, double lamda) { if(x<0 || lamda<=0) { throw new IllegalArgumentException("All the parameters must be positive."); } double probability = 1.0 - Math.exp(-lamda*x); return probability; } }
ContinuousDistributions { public static double exponentialCdf(double x, double lamda) { if(x<0 || lamda<=0) { throw new IllegalArgumentException("All the parameters must be positive."); } double probability = 1.0 - Math.exp(-lamda*x); return probability; }  }
ContinuousDistributions { public static double exponentialCdf(double x, double lamda) { if(x<0 || lamda<=0) { throw new IllegalArgumentException("All the parameters must be positive."); } double probability = 1.0 - Math.exp(-lamda*x); return probability; }  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 exponentialCdf(double x, double lamda) { if(x<0 || lamda<=0) { throw new IllegalArgumentException("All the parameters must be positive."); } double probability = 1.0 - Math.exp(-lamda*x); return probability; }  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);  }