org.apache.commons.math3.distribution
Class BetaDistribution

java.lang.Object
  extended by org.apache.commons.math3.distribution.AbstractRealDistribution
      extended by org.apache.commons.math3.distribution.BetaDistribution
All Implemented Interfaces:
Serializable, RealDistribution

public class BetaDistribution
extends AbstractRealDistribution

Implements the Beta distribution.

Since:
2.0 (changed to concrete class in 3.0)
Version:
$Id: BetaDistribution.java 1416643 2012-12-03 19:37:14Z tn $
See Also:
Beta distribution, Serialized Form

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

DEFAULT_INVERSE_ABSOLUTE_ACCURACY

public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.

Since:
2.1
See Also:
Constant Field Values
Constructor Detail

BetaDistribution

public BetaDistribution(double alpha,
                        double beta)
Build a new instance.

Parameters:
alpha - First shape parameter (must be positive).
beta - Second shape parameter (must be positive).

BetaDistribution

public BetaDistribution(double alpha,
                        double beta,
                        double inverseCumAccuracy)
Build 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:
2.1

BetaDistribution

public BetaDistribution(RandomGenerator rng,
                        double alpha,
                        double beta,
                        double inverseCumAccuracy)
Creates a β distribution.

Parameters:
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).
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

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)

isSupportLowerBoundInclusive

public boolean isSupportLowerBoundInclusive()
Whether or not the lower bound of support is in the domain of the density function. Returns true iff getSupporLowerBound() is finite and density(getSupportLowerBound()) returns a non-NaN, non-infinite value.

Returns:
true if the lower bound of support is finite and the density function returns a non-NaN, non-infinite value there

isSupportUpperBoundInclusive

public boolean isSupportUpperBoundInclusive()
Whether or not the upper bound of support is in the domain of the density function. Returns true iff getSupportUpperBound() is finite and density(getSupportUpperBound()) returns a non-NaN, non-infinite value.

Returns:
true if the upper bound of support is finite and the density function returns a non-NaN, non-infinite value there

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


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