Examples of org.apache.commons.math3.distribution.GammaDistribution.cumulativeProbability()

         *  evaluated at the random value from the distribution should match the uniform
         *  random value used to generate it, which is stored in the quantiles[] array.
         */
        for (int i = 0; i < 10; i++) {
            double value = betaDistribution.sample();
            Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
        }
    }


    @Test
    public void testNextBeta() {

         *  evaluated at the random value from the distribution should match the uniform
         *  random value used to generate it, which is stored in the quantiles[] array.
         */
        for (int i = 0; i < 10; i++) {
            double value = betaDistribution.sample();
            Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
        }
    }


    @Test
    public void testNextBeta() {

         *  evaluated at the random value from the distribution should match the uniform
         *  random value used to generate it, which is stored in the quantiles[] array.
         */
        for (int i = 0; i < 10; i++) {
            double value = betaDistribution.sample();
            Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
        }
    }


    @Test
    public void testNextBeta() {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;

        final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;
            double pTotal = 0;

        }


        final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;

        final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;
            double pTotal = 0;

            }


            final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
            switch (alternativeHypothesis) {
                case GREATER_THAN:
                    return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
                case LESS_THAN:
                    return distribution.cumulativeProbability(numberOfSuccesses);
                case TWO_SIDED:
                    int criticalValueLow = 0;
                    int criticalValueHigh = numberOfTrials;

            final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
            switch (alternativeHypothesis) {
                case GREATER_THAN:
                    return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
                case LESS_THAN:
                    return distribution.cumulativeProbability(numberOfSuccesses);
                case TWO_SIDED:
                    int criticalValueLow = 0;
                    int criticalValueHigh = numberOfTrials;
                    double pTotal = 0;


        ExponentialFamily normal = new UnivariateGaussian();
        PVector p = new PVector(1);
        p.array[0] = 32;


        System.out.println(bn.cumulativeProbability(32));
        System.out.println(n.cumulativeProbability(32));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 27;
        System.out.println(bn.cumulativeProbability(27));

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