Serializable
, RealDistribution
public class ChiSquaredDistribution extends AbstractRealDistribution
DEFAULT_SOLVER_ABSOLUTE_ACCURACY
Constructor | Description |
---|---|
ChiSquaredDistribution(double degreesOfFreedom) |
Create a Chi-Squared distribution with the given degrees of freedom.
|
ChiSquaredDistribution(double degreesOfFreedom,
double inverseCumAccuracy) |
Create a Chi-Squared distribution with the given degrees of freedom and
inverse cumulative probability accuracy.
|
Modifier and Type | Method | 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.
|
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.
|
double |
logDensity(double x) |
Returns the natural logarithm of the probability density function
(PDF) of this distribution evaluated at the specified point
x . |
getSolverAbsoluteAccuracy, inverseCumulativeProbability, probability
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
AbstractRealDistribution.DEFAULT_SOLVER_ABSOLUTE_ACCURACY
).public double getDegreesOfFreedom()
public 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 logDensity(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. Note that
due to the floating point precision and under/overflow issues, this method will
for some distributions be more precise and faster than computing the logarithm of
RealDistribution.density(double)
.
The default implementation simply computes the logarithm of density(x)
.
logDensity
in interface RealDistribution
logDensity
in class AbstractRealDistribution
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
public double getNumericalMean()
k
degrees of freedom, the mean is k
.Double.NaN
if it is not definedpublic double getNumericalVariance()
2 * k
, where k
is the number of degrees of freedom.public 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}
.
Double.POSITIVE_INFINITY
.public boolean isSupportConnected()
true
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