org.apache.commons.math3.distribution
public interface RealDistribution
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 
cumulativeProbability(double x0,
double x1)
Deprecated.
As of 3.1. In 4.0, this method will be renamed
probability(double x0, double x1) . 
double 
density(double x)
Returns the probability density function (PDF) of this distribution
evaluated at the specified point
x . 
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.

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()
Deprecated.
to be removed in 4.0

boolean 
isSupportUpperBoundInclusive()
Deprecated.
to be removed in 4.0

double 
probability(double x)
For a random variable
X whose values are distributed according
to this distribution, this method returns P(X = x) . 
void 
reseedRandomGenerator(long seed)
Reseed the random generator used to generate samples.

double 
sample()
Generate a random value sampled from this distribution.

double[] 
sample(int sampleSize)
Generate a random sample from the distribution.

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.x
 the point at which the PMF is evaluatedx
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
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
@Deprecated double cumulativeProbability(double x0, double x1) throws NumberIsTooLargeException
probability(double x0, double x1)
.X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.x0
 the exclusive lower boundx1
 the inclusive upper boundx0
and x1
,
excluding the lower and including the upper endpointNumberIsTooLargeException
 if x0 > x1
double inverseCumulativeProbability(double p) throws OutOfRangeException
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
.p
 the cumulative probabilityp
quantile of this distribution
(largest 0quantile for p = 0
)OutOfRangeException
 if p < 0
or p > 1
double getNumericalMean()
Double.NaN
if it is not defineddouble getNumericalVariance()
Double.POSITIVE_INFINITY
as
for certain cases in TDistribution
) or Double.NaN
if it
is not defineddouble getSupportLowerBound()
inverseCumulativeProbability(0)
. In other words, this
method must return
inf {x in R  P(X <= x) > 0}
.
Double.NEGATIVE_INFINITY
)double getSupportUpperBound()
inverseCumulativeProbability(1)
. In other words, this
method must return
inf {x in R  P(X <= x) = 1}
.
Double.POSITIVE_INFINITY
)@Deprecated boolean isSupportLowerBoundInclusive()
getSupporLowerBound()
is finite and
density(getSupportLowerBound())
returns a nonNaN, noninfinite
value.@Deprecated boolean isSupportUpperBoundInclusive()
getSupportUpperBound()
is finite and
density(getSupportUpperBound())
returns a nonNaN, noninfinite
value.boolean isSupportConnected()
void reseedRandomGenerator(long seed)
seed
 the new seeddouble sample()
double[] sample(int sampleSize)
sampleSize
 the number of random values to generateNotStrictlyPositiveException
 if sampleSize
is not positiveCopyright © 2003–2015 The Apache Software Foundation. All rights reserved.