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

Package org.apache.commons.math3.distribution

Class org.apache.commons.math3.distribution.BetaDistribution

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

org.apache.commons.math3.distribution.BetaDistribution.cumulativeProbability()
{@inheritDoc}

        throws NullArgumentException, DimensionMismatchException,
               ConvergenceException, MaxCountExceededException {


        final AnovaStats a = anovaStats(categoryData, allowOneElementData);
        final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
        return 1.0 - fdist.cumulativeProbability(a.F);


    }


    /**
     * This method calls the method that actually does the calculations (except

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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">

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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">

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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">

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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">

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        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));
        System.out.println(n.cumulativeProbability(27));

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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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        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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        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;
        }


        /*

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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

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