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java.lang.Objectorg.apache.commons.math3.distribution.AbstractRealDistribution
org.apache.commons.math3.distribution.BetaDistribution
public class BetaDistribution
Implements the Beta distribution.
| Field Summary | |
|---|---|
static double |
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy. |
| Fields inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution |
|---|
random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY |
| Constructor Summary | |
|---|---|
BetaDistribution(double alpha,
double beta)
Build a new instance. |
|
BetaDistribution(double alpha,
double beta,
double inverseCumAccuracy)
Build a new instance. |
|
BetaDistribution(RandomGenerator rng,
double alpha,
double beta,
double inverseCumAccuracy)
Creates a β distribution. |
|
| Method Summary | |
|---|---|
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()
Whether or not the lower bound of support is in the domain of the density function. |
boolean |
isSupportUpperBoundInclusive()
Whether or not the upper bound of support is in the domain of the density function. |
| Methods inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution |
|---|
cumulativeProbability, inverseCumulativeProbability, probability, probability, reseedRandomGenerator, sample, sample |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
| Constructor Detail |
|---|
public BetaDistribution(double alpha,
double beta)
alpha - First shape parameter (must be positive).beta - Second shape parameter (must be positive).
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(RandomGenerator rng,
double alpha,
double beta,
double inverseCumAccuracy)
rng - Random number generator.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).| Method Detail |
|---|
public double getAlpha()
alpha.
public double getBeta()
beta.
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 evaluated
xpublic 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 evaluated
xprotected double getSolverAbsoluteAccuracy()
getSolverAbsoluteAccuracy in class AbstractRealDistributionpublic 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()
getSupporLowerBound() is finite and
density(getSupportLowerBound()) returns a non-NaN, non-infinite
value.
public boolean isSupportUpperBoundInclusive()
getSupportUpperBound() is finite and
density(getSupportUpperBound()) returns a non-NaN, non-infinite
value.
public boolean isSupportConnected()
true
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