Examples of cumulativeProbability()

@param x the value at which the CDF is evaluated. @return CDF for this distribution. @throws MathException if the cumulative probability can not becomputed due to convergence or other numerical errors.
  • org.apache.commons.math.distribution.NormalDistributionImpl.cumulativeProbability()
    For this distribution, X, this method returns P(X < x). If xis more than 40 standard deviations from the mean, 0 or 1 is returned, as in these cases the actual value is within Double.MIN_VALUE of 0 or 1. @param x the value at which the CDF is evaluated. @return CDF evaluated at x. @throws MathException if the algorithm fails to converge
  • org.apache.commons.math.distribution.PoissonDistribution.cumulativeProbability()
  • org.apache.commons.math.distribution.PoissonDistributionImpl.cumulativeProbability()
    The probability distribution function P(X <= x) for a Poisson distribution. @param x the value at which the PDF is evaluated. @return Poisson distribution function evaluated at x @throws MathException if the cumulative probability can not be computeddue to convergence or other numerical errors.
  • org.apache.commons.math.distribution.TDistribution.cumulativeProbability()
  • org.apache.commons.math.distribution.TDistributionImpl.cumulativeProbability()
    For this distribution, X, this method returns P(X < x). @param x the value at which the CDF is evaluated. @return CDF evaluated at x. @throws MathException if the cumulative probability can not becomputed due to convergence or other numerical errors.
  • org.apache.commons.math.distribution.WeibullDistribution.cumulativeProbability()
  • org.apache.commons.math3.distribution.BetaDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.BinomialDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.ChiSquaredDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.FDistribution.cumulativeProbability()
    orld.wolfram.com/F-Distribution.html"> F-Distribution, equation (4).
  • org.apache.commons.math3.distribution.GammaDistribution.cumulativeProbability()
    orld.wolfram.com/Chi-SquaredDistribution.html"> Chi-Squared Distribution, equation (9).
  • Casella, G., & Berger, R. (1990). Statistical Inference. Belmont, CA: Duxbury Press.
  • org.apache.commons.math3.distribution.IntegerDistribution.cumulativeProbability()
    For a random variable {@code X} whose values are distributed accordingto this distribution, this method returns {@code P(X <= x)}. In other words, this method represents the (cumulative) distribution function (CDF) for this distribution. @param x the point at which the CDF is evaluated @return the probability that a random variable with thisdistribution takes a value less than or equal to {@code x}
  • org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()
    {@inheritDoc}If {@code x} is more than 40 standard deviations from the mean, 0 or 1is returned, as in these cases the actual value is within {@code Double.MIN_VALUE} of 0 or 1.
  • org.apache.commons.math3.distribution.PoissonDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.RealDistribution.cumulativeProbability()
    For a random variable {@code X} whose values are distributed accordingto this distribution, this method returns {@code P(x0 < X <= x1)}. @param x0 the exclusive lower bound @param x1 the inclusive upper bound @return the probability that a random variable with this distributiontakes a value between {@code x0} and {@code x1}, excluding the lower and including the upper endpoint @throws NumberIsTooLargeException if {@code x0> x1} @deprecated As of 3.1. In 4.0, this method will be renamed{@code probability(double x0, double x1)}.
  • org.apache.commons.math3.distribution.TDistribution.cumulativeProbability()
    {@inheritDoc}

    • Examples of org.apache.commons.math.distribution.PoissonDistributionImpl.cumulativeProbability()

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             *  Start with upper and lower tail bins.
             *  Lower bin = [0, lower); Upper bin = [upper, +inf).
             */
            PoissonDistribution poissonDistribution = new PoissonDistributionImpl(mean);
            int lower = 1;
            while (poissonDistribution.cumulativeProbability(lower - 1) * sampleSize < minExpectedCount) {
                lower++;
            }
            int upper = (int) (5 * mean);  // Even for mean = 1, not much mass beyond 5
            while ((1 - poissonDistribution.cumulativeProbability(upper - 1)) * sampleSize < minExpectedCount) {
                upper--;
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    Examples of org.apache.commons.math.distribution.TDistribution.cumulativeProbability()

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            RealMatrix pValues = corrInstance.getCorrelationPValues();
            RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
            for (int i = 0; i < 5; i++) {
                for (int j = 0; j < i; j++) {
                    double t = Math.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                    double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                    assertEquals(p, pValues.getEntry(i, j), 10E-15);
                }
            }
        }

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    Examples of org.apache.commons.math.distribution.TDistributionImpl.cumulativeProbability()

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            RealMatrix pValues = corrInstance.getCorrelationPValues();
            RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
            for (int i = 0; i < 5; i++) {
                for (int j = 0; j < i; j++) {
                    double t = Math.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                    double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                    assertEquals(p, pValues.getEntry(i, j), 10E-15);
                }
            }
        }

    View Full Code Here

    Examples of org.apache.commons.math.distribution.WeibullDistribution.cumulativeProbability()

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        double x = CommonFns.toNumber(args[0]).doubleValue();
        double alpha = CommonFns.toNumber(args[1]).doubleValue();
        double beta = CommonFns.toNumber(args[1]).doubleValue();
        DistributionFactory factory = DistributionFactory.newInstance();
        WeibullDistribution wb = factory.createWeibullDistribution(alpha, beta);
        return UtilFns.validateNumber(wb.cumulativeProbability(x));
      }
     
      /**
       * Returns the sum of deviation
       * @param total total
    View Full Code Here

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

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             *  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() {
    View Full Code Here

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

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            // 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;
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    Examples of org.apache.commons.math3.distribution.BinomialDistribution.cumulativeProbability()

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            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;
    View Full Code Here

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

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            DimensionMismatchException, MaxCountExceededException {

            // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
            final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(null, expected.length - 1.0);
            return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
        }

        /**
         * Performs a <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
         * Chi-square goodness of fit test</a> evaluating the null hypothesis that the
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    Examples of org.apache.commons.math3.distribution.ChiSquaredDistribution.cumulativeProbability()

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            checkArray(counts);
            double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
            // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
            final ChiSquaredDistribution distribution = new ChiSquaredDistribution(df);
            return 1 - distribution.cumulativeProbability(chiSquare(counts));

        }

        /**
         * Performs a <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">
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    Examples of org.apache.commons.math3.distribution.ChiSquaredDistribution.cumulativeProbability()

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            MaxCountExceededException {

            // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
            final ChiSquaredDistribution distribution =
                    new ChiSquaredDistribution(null, (double) observed1.length - 1);
            return 1 - distribution.cumulativeProbability(
                    chiSquareDataSetsComparison(observed1, observed2));

        }

        /**
     
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