public class ChiSquaredDistribution extends AbstractRealDistribution
Modifier and Type  Field and Description 

static double 
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy

randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY
Constructor and Description 

ChiSquaredDistribution(double degreesOfFreedom)
Create a ChiSquared distribution with the given degrees of freedom.

ChiSquaredDistribution(double degreesOfFreedom,
double inverseCumAccuracy)
Create a ChiSquared distribution with the given degrees of freedom and
inverse cumulative probability accuracy.

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 
getDegreesOfFreedom()
Access the number of degrees of freedom.

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.

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.

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) . 
cumulativeProbability, inverseCumulativeProbability, reseedRandomGenerator, sample, sample
public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
public ChiSquaredDistribution(double degreesOfFreedom)
degreesOfFreedom
 Degrees of freedom.public ChiSquaredDistribution(double degreesOfFreedom, double inverseCumAccuracy)
degreesOfFreedom
 Degrees of freedom.inverseCumAccuracy
 the maximum absolute error in inverse
cumulative probability estimates (defaults to
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
).public double getDegreesOfFreedom()
public double probability(double x)
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.x
 the point at which the PMF is evaluatedpublic double density(double x)
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.x
 the point at which the PDF is evaluatedx
public double cumulativeProbability(double x)
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.x
 the point at which the CDF is evaluatedx
protected double getSolverAbsoluteAccuracy()
getSolverAbsoluteAccuracy
in class AbstractRealDistribution
public double getNumericalMean()
k
degrees of freedom, the mean is k
.Double.NaN
if it is not definedpublic double getNumericalVariance()
k
degrees of freedom, the variance is 2 * k
.Double.POSITIVE_INFINITY
as
for certain cases in TDistribution
) or Double.NaN
if it
is not definedpublic double getSupportLowerBound()
inverseCumulativeProbability(0)
. In other words, this
method must return
inf {x in R  P(X <= x) > 0}
.
public double getSupportUpperBound()
inverseCumulativeProbability(1)
. In other words, this
method must return
inf {x in R  P(X <= x) = 1}
.
public boolean isSupportLowerBoundInclusive()
public boolean isSupportUpperBoundInclusive()
public boolean isSupportConnected()
true
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