org.apache.commons.math4.distribution

• ### Nested classes/interfaces inherited from interface org.apache.commons.math4.distribution.RealDistribution

RealDistribution.Sampler
• ### Field Summary

Fields
Modifier and Type Field and Description
static double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
• ### Fields inherited from class org.apache.commons.math4.distribution.AbstractRealDistribution

SOLVER_DEFAULT_ABSOLUTE_ACCURACY
• ### Constructor Summary

Constructors
Constructor and Description
BetaDistribution(double alpha, double beta)
Creates a new instance.
BetaDistribution(double alpha, double beta, double inverseCumAccuracy)
Creates a new instance.
• ### Method Summary

All Methods
Modifier and Type Method and Description
RealDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
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.
double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.
• ### Methods inherited from class org.apache.commons.math4.distribution.AbstractRealDistribution

inverseCumulativeProbability, probability, probability, sample
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Field Detail

• #### DEFAULT_INVERSE_ABSOLUTE_ACCURACY

public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
Since:
2.1
Constant Field Values
• ### Constructor Detail

public BetaDistribution(double alpha,
double beta)
Creates a new instance.
Parameters:
alpha - First shape parameter (must be positive).
beta - Second shape parameter (must be positive).

public BetaDistribution(double alpha,
double beta,
double inverseCumAccuracy)
Creates a new instance.
Parameters:
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).
Since:
3.1
• ### Method Detail

• #### getAlpha

public double getAlpha()
Access the first shape parameter, alpha.
Returns:
the first shape parameter.
• #### getBeta

public double getBeta()
Access the second shape parameter, beta.
Returns:
the second shape parameter.
• #### density

public double density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point 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.
Parameters:
x - the point at which the PDF is evaluated
Returns:
the value of the probability density function at point x
• #### logDensity

public double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point 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).

Specified by:
logDensity in interface RealDistribution
Overrides:
logDensity in class AbstractRealDistribution
Parameters:
x - the point at which the PDF is evaluated
Returns:
the logarithm of the value of the probability density function at point x
• #### cumulativeProbability

public double cumulativeProbability(double x)
For a random variable 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.
Parameters:
x - the point at which the CDF is evaluated
Returns:
the probability that a random variable with this distribution takes a value less than or equal to x
• #### getSolverAbsoluteAccuracy

protected double getSolverAbsoluteAccuracy()
Return the absolute accuracy setting of the solver used to estimate inverse cumulative probabilities.
Overrides:
getSolverAbsoluteAccuracy in class AbstractRealDistribution
Returns:
the solver absolute accuracy.
Since:
2.1
• #### getNumericalMean

public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. For first shape parameter alpha and second shape parameter beta, the mean is alpha / (alpha + beta).
Returns:
the mean or Double.NaN if it is not defined
• #### getNumericalVariance

public double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution. For first shape parameter alpha and second shape parameter beta, the variance is (alpha * beta) / [(alpha + beta)^2 * (alpha + beta + 1)].
Returns:
the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistribution) or Double.NaN if it is not defined
• #### getSupportLowerBound

public double getSupportLowerBound()
Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

inf {x in R | P(X <= x) > 0}.

The lower bound of the support is always 0 no matter the parameters.
Returns:
lower bound of the support (always 0)
• #### getSupportUpperBound

public double getSupportUpperBound()
Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

inf {x in R | P(X <= x) = 1}.

The upper bound of the support is always 1 no matter the parameters.
Returns:
upper bound of the support (always 1)
• #### isSupportConnected

public boolean isSupportConnected()
Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
Returns:
true
• #### createSampler

public RealDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler. Sampling is performed using Cheng's algorithm:
 R. C. H. Cheng,
"Generating beta variates with nonintegral shape parameters",
Communications of the ACM, 21, 317-322, 1978.

Specified by:
createSampler in interface RealDistribution
Overrides:
createSampler in class AbstractRealDistribution
Parameters:
rng - Generator of uniformly distributed numbers.
Returns:
a sampler that produces random numbers according this distribution.