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.math3.distribution.IntegerDistribution.cumulativeProbability()

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          int totalNew = testCount + newTrainInGen;

          IntegerDistribution dist = new BinomialDistribution(random, totalNew, TEST_FRACTION);
          double probability;
          if (testCount < dist.getNumericalMean()) {
            probability = dist.cumulativeProbability(testCount);
          } else {
            probability = 1.0 - dist.cumulativeProbability(testCount);
          }
          log.info("Probability of observing {} as {} sample of {}: {}",
                   testCount, TEST_FRACTION, totalNew, probability);
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    Examples of org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()

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            // No try-catch or advertised exception because args are valid
            // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
            final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);

            return 2*standardNormal.cumulativeProbability(z);
        }

        /**
         * Returns the <i>observed significance level</i>, or <a href=
         * "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
     
    View Full Code Here

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

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            // No try-catch or advertised exception because args are valid
            // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
            final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);

            return 2 * standardNormal.cumulativeProbability(z);
        }

        /**
         * Returns the asymptotic <i>observed significance level</i>, or <a href=
         * "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
     
    View Full Code Here

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

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            final double z = (Umin - EU) / FastMath.sqrt(VarU);

            final NormalDistribution standardNormal = new NormalDistribution(0, 1);

            return 2 * standardNormal.cumulativeProbability(z);
        }

        /**
         * Returns the asymptotic <i>observed significance level</i>, or <a href=
         * "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
     
    View Full Code Here

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

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            // - 0.5 is a continuity correction
            final double z = (Wmin - ES - 0.5) / FastMath.sqrt(VarS);

            final NormalDistribution standardNormal = new NormalDistribution(0, 1);

            return 2*standardNormal.cumulativeProbability(z);
        }

        /**
         * Returns the <i>observed significance level</i>, or <a href=
         * "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
     
    View Full Code Here

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

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             *  Start with upper and lower tail bins.
             *  Lower bin = [0, lower); Upper bin = [upper, +inf).
             */
            PoissonDistribution poissonDistribution = new PoissonDistribution(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.math3.distribution.PoissonDistribution.cumulativeProbability()

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            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--;
            }

            // Set bin width for interior bins.  For poisson, only need to look at end bins.
            int binWidth = 0;
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    Examples of org.apache.commons.math3.distribution.PoissonDistribution.cumulativeProbability()

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            boolean widthSufficient = false;
            double lowerBinMass = 0;
            double upperBinMass = 0;
            while (!widthSufficient) {
                binWidth++;
                lowerBinMass = poissonDistribution.cumulativeProbability(lower - 1, lower + binWidth - 1);
                upperBinMass = poissonDistribution.cumulativeProbability(upper - binWidth - 1, upper - 1);
                widthSufficient = FastMath.min(lowerBinMass, upperBinMass) * sampleSize >= minExpectedCount;
            }

            /*
     
    View Full Code Here

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

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            double lowerBinMass = 0;
            double upperBinMass = 0;
            while (!widthSufficient) {
                binWidth++;
                lowerBinMass = poissonDistribution.cumulativeProbability(lower - 1, lower + binWidth - 1);
                upperBinMass = poissonDistribution.cumulativeProbability(upper - binWidth - 1, upper - 1);
                widthSufficient = FastMath.min(lowerBinMass, upperBinMass) * sampleSize >= minExpectedCount;
            }

            /*
             *  Determine interior bin bounds.  Bins are
    View Full Code Here

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

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            // Bottom bin
            observed[0] = 0;
            for (int i = 0; i < lower; i++) {
                observed[0] += frequency.getCount(i);
            }
            expected[0] = poissonDistribution.cumulativeProbability(lower - 1) * sampleSize;

            // Top bin
            observed[binCount - 1] = 0;
            for (int i = upper; i <= maxObservedValue; i++) {
                observed[binCount - 1] += frequency.getCount(i);
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