List of inverseCumulativeProbability() Examples

Examples of inverseCumulativeProbability()

org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns 0 for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.IntegerDistribution.inverseCumulativeProbability()
For this distribution, X, this method returns the largest x such that P(X ≤ x) <= p.
Note that this definition implies:
- If there is a minimum value, m, with positive probability under (the density of) X, then m - 1 is returned by inverseCumulativeProbability(0). If there is no such value m, Integer.MIN_VALUE is returned.
- If there is a maximum value, M, such that P(X ≤ M) =1, then M is returned by inverseCumulativeProbability(1). If there is no such value, M, Integer.MAX_VALUE is returned.
@param p the cumulative probability. @return the largest x such that P(X ≤ x) <= p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not between 0 and 1 (inclusive)
org.apache.commons.math.distribution.NormalDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.PoissonDistributionImpl.inverseCumulativeProbability()
org.apache.commons.math.distribution.TDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.TDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.ZipfDistributionImpl.inverseCumulativeProbability()
org.apache.commons.math3.distribution.BinomialDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.ExponentialDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()
{@inheritDoc} @since 3.2
org.apache.commons.math3.distribution.RealDistribution.inverseCumulativeProbability()
Computes the quantile function of this distribution. For a random variable {@code X} distributed according to this distribution, thereturned value is
- inf{x in R | P(X<=x)>= p} for {@code 0 < p <= 1},
- inf{x in R | P(X<=x)> 0} for {@code p = 0}.
@param p the cumulative probability @return the smallest {@code p}-quantile of this distribution (largest 0-quantile for {@code p = 0}) @throws OutOfRangeException if {@code p < 0} or {@code p> 1}
org.apache.commons.math3.distribution.TDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.WeibullDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p == 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.

Examples of org.apache.commons.math3.distribution.WeibullDistribution.inverseCumulativeProbability()

                final long max = Long.parseLong(bounds[1]);
                final double shape = Double.parseDouble(params.get(1));
                WeibullDistribution findBounds = new WeibullDistribution(shape, 1d);
                // max probability should be roughly equal to accuracy of (max-min) to ensure all values are visitable,
                // over entire range, but this results in overly skewed distribution, so take sqrt
                final double scale = (max - min) / findBounds.inverseCumulativeProbability(1d - Math.sqrt(1d/(max-min)));
                return new ExtremeFactory(min, max, shape, scale);
            } catch (Exception _)
            {
                throw new IllegalArgumentException("Invalid parameter list for extreme (Weibull) distribution: " + params);
            }

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Examples of org.apache.commons.math3.distribution.WeibullDistribution.inverseCumulativeProbability()

                final long max = Long.parseLong(bounds[1]);
                final double shape = Double.parseDouble(params.get(1));
                WeibullDistribution findBounds = new WeibullDistribution(shape, 1d);
                // max probability should be roughly equal to accuracy of (max-min) to ensure all values are visitable,
                // over entire range, but this results in overly skewed distribution, so take sqrt
                final double scale = (max - min) / findBounds.inverseCumulativeProbability(1d - Math.sqrt(1d/(max-min)));
                return new ExtremeFactory(min, max, shape, scale);
            } catch (Exception _)
            {
                throw new IllegalArgumentException("Invalid parameter list for extreme (Weibull) distribution: " + params);
            }

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