public class BetaDistribution 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 |
---|
BetaDistribution(double alpha,
double beta)
Build a new instance.
|
BetaDistribution(double alpha,
double beta,
double inverseCumAccuracy)
Build a new instance.
|
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 |
getAlpha()
Access the first shape parameter,
alpha . |
double |
getBeta()
Access the second shape parameter,
beta . |
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()
Return the absolute accuracy setting of the solver used to estimate
inverse cumulative probabilities.
|
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 BetaDistribution(double alpha, double beta, double inverseCumAccuracy)
alpha
- First shape parameter (must be positive).beta
- Second shape parameter (must be positive).inverseCumAccuracy
- Maximum absolute error in inverse
cumulative probability estimates (defaults to
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
).public BetaDistribution(double alpha, double beta)
alpha
- First shape parameter (must be positive).beta
- Second shape parameter (must be positive).public double getAlpha()
alpha
.public double getBeta()
beta
.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()
alpha
and second shape parameter
beta
, the mean is alpha / (alpha + beta)
.Double.NaN
if it is not definedpublic double getNumericalVariance()
alpha
and second shape parameter
beta
, the variance is
(alpha * beta) / [(alpha + beta)^2 * (alpha + beta + 1)]
.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
Copyright © 2003-2012 Apache Software Foundation. All Rights Reserved.