org.apache.commons.math3.distribution

## Class CauchyDistribution

• ### Field Summary

Fields
Modifier and Type Field and Description
`static double` `DEFAULT_INVERSE_ABSOLUTE_ACCURACY`
Default inverse cumulative probability accuracy.
• ### Fields inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution

`randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY`
• ### Constructor Summary

Constructors
Constructor and Description
`CauchyDistribution()`
Creates a Cauchy distribution with the median equal to zero and scale equal to one.
```CauchyDistribution(double median, double scale)```
Creates a Cauchy distribution using the given median and scale.
```CauchyDistribution(double median, double scale, double inverseCumAccuracy)```
Creates a Cauchy distribution using the given median and scale.
• ### Method Summary

Methods
Modifier and Type Method and Description
`double` `cumulativeProbability(double x)`
For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X <= x)`.
`double` `density(double x)`
Returns the probability density function (PDF) of this distribution evaluated at the specified point `x`.
`double` `getMedian()`
Access the median.
`double` `getNumericalMean()`
Use this method to get the numerical value of the mean of this distribution.
`double` `getNumericalVariance()`
Use this method to get the numerical value of the variance of this distribution.
`double` `getScale()`
Access the scale parameter.
`protected double` `getSolverAbsoluteAccuracy()`
Returns the solver absolute accuracy for inverse cumulative computation.
`double` `getSupportLowerBound()`
Access the lower bound of the support.
`double` `getSupportUpperBound()`
Access the upper bound of the support.
`double` `inverseCumulativeProbability(double p)`
Computes the quantile function of this distribution.
`boolean` `isSupportConnected()`
Use this method to get information about whether the support is connected, i.e.
`boolean` `isSupportLowerBoundInclusive()`
Use this method to get information about whether the lower bound of the support is inclusive or not.
`boolean` `isSupportUpperBoundInclusive()`
Use this method to get information about whether the upper bound of the support is inclusive or not.
`double` `probability(double x)`
For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X = x)`.
• ### Methods inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution

`cumulativeProbability, reseedRandomGenerator, sample, sample`
• ### Methods inherited from class java.lang.Object

`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Field Detail

• #### DEFAULT_INVERSE_ABSOLUTE_ACCURACY

`public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY`
Default inverse cumulative probability accuracy.
Since:
2.1
Constant Field Values
• ### Constructor Detail

• #### CauchyDistribution

`public CauchyDistribution()`
Creates a Cauchy distribution with the median equal to zero and scale equal to one.
• #### CauchyDistribution

```public CauchyDistribution(double median,
double scale)```
Creates a Cauchy distribution using the given median and scale.
Parameters:
`median` - Median for this distribution.
`scale` - Scale parameter for this distribution.
• #### CauchyDistribution

```public CauchyDistribution(double median,
double scale,
double inverseCumAccuracy)```
Creates a Cauchy distribution using the given median and scale.
Parameters:
`median` - Median for this distribution.
`scale` - Scale parameter for this distribution.
`inverseCumAccuracy` - Maximum absolute error in inverse cumulative probability estimates (defaults to `DEFAULT_INVERSE_ABSOLUTE_ACCURACY`).
Throws:
`NotStrictlyPositiveException` - if `scale <= 0`.
Since:
2.1
• ### Method Detail

• #### cumulativeProbability

`public double cumulativeProbability(double x)`
For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X <= x)`. In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.
Parameters:
`x` - the point at which the CDF is evaluated
Returns:
the probability that a random variable with this distribution takes a value less than or equal to `x`
• #### getMedian

`public double getMedian()`
Access the median.
Returns:
the median for this distribution.
• #### getScale

`public double getScale()`
Access the scale parameter.
Returns:
the scale parameter for this distribution.
• #### probability

`public double probability(double x)`
For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X = x)`. In other words, this method represents the probability mass function (PMF) for the distribution. For this distribution `P(X = x)` always evaluates to 0.
Parameters:
`x` - the point at which the PMF is evaluated
Returns:
0
• #### density

`public double density(double x)`
Returns the probability density function (PDF) of this distribution evaluated at the specified point `x`. In general, the PDF is the derivative of the `CDF`. If the derivative does not exist at `x`, then an appropriate replacement should be returned, e.g. `Double.POSITIVE_INFINITY`, `Double.NaN`, or the limit inferior or limit superior of the difference quotient.
Parameters:
`x` - the point at which the PDF is evaluated
Returns:
the value of the probability density function at point `x`
• #### inverseCumulativeProbability

```public double inverseCumulativeProbability(double p)
throws OutOfRangeException```
Computes the quantile function of this distribution. For a random variable `X` distributed according to this distribution, the returned value is
• `inf{x in R | P(X<=x) >= p}` for `0 < p <= 1`,
• `inf{x in R | P(X<=x) > 0}` for `p = 0`.
The default implementation returns Returns `Double.NEGATIVE_INFINITY` when `p == 0` and `Double.POSITIVE_INFINITY` when `p == 1`.
Specified by:
`inverseCumulativeProbability` in interface `RealDistribution`
Overrides:
`inverseCumulativeProbability` in class `AbstractRealDistribution`
Parameters:
`p` - the cumulative probability
Returns:
the smallest `p`-quantile of this distribution (largest 0-quantile for `p = 0`)
Throws:
`OutOfRangeException` - if `p < 0` or `p > 1`
• #### getSolverAbsoluteAccuracy

`protected double getSolverAbsoluteAccuracy()`
Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
Overrides:
`getSolverAbsoluteAccuracy` in class `AbstractRealDistribution`
Returns:
the maximum absolute error in inverse cumulative probability estimates
• #### getNumericalMean

`public double getNumericalMean()`
Use this method to get the numerical value of the mean of this distribution. The mean is always undefined no matter the parameters.
Returns:
mean (always Double.NaN)
• #### getNumericalVariance

`public double getNumericalVariance()`
Use this method to get the numerical value of the variance of this distribution. The variance is always undefined no matter the parameters.
Returns:
variance (always Double.NaN)
• #### getSupportLowerBound

`public double getSupportLowerBound()`
Access the lower bound of the support. This method must return the same value as `inverseCumulativeProbability(0)`. In other words, this method must return

`inf {x in R | P(X <= x) > 0}`.

The lower bound of the support is always negative infinity no matter the parameters.
Returns:
lower bound of the support (always Double.NEGATIVE_INFINITY)
• #### getSupportUpperBound

`public double getSupportUpperBound()`
Access the upper bound of the support. This method must return the same value as `inverseCumulativeProbability(1)`. In other words, this method must return

`inf {x in R | P(X <= x) = 1}`.

The upper bound of the support is always positive infinity no matter the parameters.
Returns:
upper bound of the support (always Double.POSITIVE_INFINITY)
• #### isSupportLowerBoundInclusive

`public boolean isSupportLowerBoundInclusive()`
Use this method to get information about whether the lower bound of the support is inclusive or not.
Returns:
whether the lower bound of the support is inclusive or not
• #### isSupportUpperBoundInclusive

`public boolean isSupportUpperBoundInclusive()`
Use this method to get information about whether the upper bound of the support is inclusive or not.
Returns:
whether the upper bound of the support is inclusive or not
• #### isSupportConnected

`public boolean isSupportConnected()`
Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
Returns:
`true`