org.apache.commons.statistics.distribution

• All Implemented Interfaces:
ContinuousDistribution

public final class BetaDistribution
extends Object
Implementation of the beta distribution.

The probability density function of $$X$$ is:

$f(x; \alpha, \beta) = \frac{1}{ B(\alpha, \beta)} x^{\alpha-1} (1-x)^{\beta-1}$

for $$\alpha > 0$$, $$\beta > 0$$, $$x \in [0, 1]$$, and the beta function, $$B$$, is a normalization constant:

$B(\alpha, \beta) = \frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha) \Gamma(\beta)}$

where $$\Gamma$$ is the gamma function.

$$\alpha$$ and $$\beta$$ are shape parameters.

Beta distribution (Wikipedia), Beta distribution (MathWorld)

• ### Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution

ContinuousDistribution.Sampler
• ### Method Summary

All Methods
Modifier and Type Method and Description
ContinuousDistribution.Sampler createSampler(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()
Gets the first shape parameter of this distribution.
double getBeta()
Gets the second shape parameter of this distribution.
double getMean()
Gets the mean of this distribution.
double getSupportLowerBound()
Gets the lower bound of the support.
double getSupportUpperBound()
Gets the upper bound of the support.
double getVariance()
Gets the variance of this distribution.
double inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
double inverseSurvivalProbability(double p)
Computes the inverse survival probability function of this distribution.
double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.
static BetaDistribution of(double alpha, double beta)
Creates a beta distribution.
double probability(double x0, double x1)
For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
double survivalProbability(double x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X > x).
• ### Methods inherited from class java.lang.Object

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

• #### of

public static BetaDistribution of(double alpha,
double beta)
Creates a beta distribution.
Parameters:
alpha - First shape parameter (must be positive).
beta - Second shape parameter (must be positive).
Returns:
the distribution
Throws:
IllegalArgumentException - if alpha <= 0 or beta <= 0.
• #### getAlpha

public double getAlpha()
Gets the first shape parameter of this distribution.
Returns:
the first shape parameter.
• #### getBeta

public double getBeta()
Gets the second shape parameter of this distribution.
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.

The density is not defined when x = 0, alpha < 1, or x = 1, beta < 1. In this case the limit of infinity is returned.

Parameters:
x - Point at which the PDF is evaluated.
Returns:
the value of the probability density function at 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.

The density is not defined when x = 0, alpha < 1, or x = 1, beta < 1. In this case the limit of infinity is returned.

Parameters:
x - Point at which the PDF is evaluated.
Returns:
the logarithm of the value of the probability density function at 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 - 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.
• #### survivalProbability

public double survivalProbability(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 complementary cumulative distribution function.

By default, this is defined as 1 - cumulativeProbability(x), but the specific implementation may be more accurate.

Parameters:
x - Point at which the survival function is evaluated.
Returns:
the probability that a random variable with this distribution takes a value greater than x.
• #### getMean

public double getMean()
Gets the mean of this distribution.

For first shape parameter $$\alpha$$ and second shape parameter $$\beta$$, the mean is:

$\frac{\alpha}{\alpha + \beta}$

Returns:
the mean.
• #### getVariance

public double getVariance()
Gets the variance of this distribution.

For first shape parameter $$\alpha$$ and second shape parameter $$\beta$$, the variance is:

$\frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta + 1)}$.

Returns:
the variance.
• #### getSupportLowerBound

public double getSupportLowerBound()
Gets the lower bound of the support. It must return the same value as inverseCumulativeProbability(0), i.e. $$\inf \{ x \in \mathbb R : P(X \le x) \gt 0 \}$$.

The lower bound of the support is always 0.

Returns:
0.
• #### getSupportUpperBound

public double getSupportUpperBound()
Gets the upper bound of the support. It must return the same value as inverseCumulativeProbability(1), i.e. $$\inf \{ x \in \mathbb R : P(X \le x) = 1 \}$$.

The upper bound of the support is always 1.

Returns:
1.
• #### createSampler

public ContinuousDistribution.Sampler createSampler(UniformRandomProvider rng)
Creates a sampler.
Specified by:
createSampler in interface ContinuousDistribution
Parameters:
rng - Generator of uniformly distributed numbers.
Returns:
a sampler that produces random numbers according this distribution.
• #### probability

public double probability(double x0,
double x1)
For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
Specified by:
probability in interface ContinuousDistribution
Parameters:
x0 - Lower bound (exclusive).
x1 - Upper bound (inclusive).
Returns:
the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint.
• #### inverseCumulativeProbability

public double inverseCumulativeProbability(double p)
Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is:

$x = \begin{cases} \inf \{ x \in \mathbb R : P(X \le x) \ge p\} & \text{for } 0 \lt p \le 1 \\ \inf \{ x \in \mathbb R : P(X \le x) \gt 0 \} & \text{for } p = 0 \end{cases}$

The default implementation returns:

Specified by:
inverseCumulativeProbability in interface ContinuousDistribution
Parameters:
p - Cumulative probability.
Returns:
the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
Throws:
IllegalArgumentException - if p < 0 or p > 1
• #### inverseSurvivalProbability

public double inverseSurvivalProbability(double p)
Computes the inverse survival probability function of this distribution. For a random variable X distributed according to this distribution, the returned value is:

$x = \begin{cases} \inf \{ x \in \mathbb R : P(X \ge x) \le p\} & \text{for } 0 \le p \lt 1 \\ \inf \{ x \in \mathbb R : P(X \ge x) \lt 1 \} & \text{for } p = 1 \end{cases}$

By default, this is defined as inverseCumulativeProbability(1 - p), but the specific implementation may be more accurate.

The default implementation returns:

Specified by:
inverseSurvivalProbability in interface ContinuousDistribution
Parameters:
p - Survival probability.
Returns:
the smallest (1-p)-quantile of this distribution (largest 0-quantile for p = 1).
Throws:
IllegalArgumentException - if p < 0 or p > 1