org.apache.commons.statistics.distribution

## Class BinomialDistribution

• java.lang.Object
• org.apache.commons.statistics.distribution.BinomialDistribution
• All Implemented Interfaces:
DiscreteDistribution

public final class BinomialDistribution
extends Object
Implementation of the binomial distribution.

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

$f(k; n, p) = \binom{n}{k} p^k (1-p)^{n-k}$

for $$n \in \{0, 1, 2, \dots\}$$ the number of trials, $$p \in [0, 1]$$ the probability of success, $$k \in \{0, 1, \dots, n\}$$ the number of successes, and

$\binom{n}{k} = \frac{n!}{k! \, (n-k)!}$

is the binomial coefficient.

Binomial distribution (Wikipedia), Binomial distribution (MathWorld)

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

DiscreteDistribution.Sampler
• ### Method Summary

All Methods
Modifier and Type Method and Description
DiscreteDistribution.Sampler createSampler(UniformRandomProvider rng)
Creates a sampler.
double cumulativeProbability(int x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
double getMean()
Gets the mean of this distribution.
int getNumberOfTrials()
Gets the number of trials parameter of this distribution.
double getProbabilityOfSuccess()
Gets the probability of success parameter of this distribution.
int getSupportLowerBound()
Gets the lower bound of the support.
int getSupportUpperBound()
Gets the upper bound of the support.
double getVariance()
Gets the variance of this distribution.
int inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
int inverseSurvivalProbability(double p)
Computes the inverse survival probability function of this distribution.
double logProbability(int x)
For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm.
static BinomialDistribution of(int trials, double p)
Creates a binomial distribution.
double probability(int x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X = x).
double probability(int x0, int x1)
For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
double survivalProbability(int 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 BinomialDistribution of(int trials,
double p)
Creates a binomial distribution.
Parameters:
trials - Number of trials.
p - Probability of success.
Returns:
the distribution
Throws:
IllegalArgumentException - if trials < 0, or if p < 0 or p > 1.
• #### getNumberOfTrials

public int getNumberOfTrials()
Gets the number of trials parameter of this distribution.
Returns:
the number of trials.
• #### getProbabilityOfSuccess

public double getProbabilityOfSuccess()
Gets the probability of success parameter of this distribution.
Returns:
the probability of success.
• #### probability

public double probability(int 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 probability mass function (PMF) for the distribution.
Parameters:
x - Point at which the PMF is evaluated.
Returns:
the value of the probability mass function at x.
• #### logProbability

public double logProbability(int x)
For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm.
Parameters:
x - Point at which the PMF is evaluated.
Returns:
the logarithm of the value of the probability mass function at x.
• #### cumulativeProbability

public double cumulativeProbability(int 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(int 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 number of trials $$n$$ and probability of success $$p$$, the mean is $$np$$.

Returns:
the mean.
• #### getVariance

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

For number of trials $$n$$ and probability of success $$p$$, the variance is $$np (1 - p)$$.

Returns:
the variance.
• #### getSupportLowerBound

public int getSupportLowerBound()
Gets the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0), i.e. $$\inf \{ x \in \mathbb Z : P(X \le x) \gt 0 \}$$. By convention, Integer.MIN_VALUE should be substituted for negative infinity.

The lower bound of the support is always 0 except for the probability parameter p = 1.

Returns:
0 or the number of trials.
• #### getSupportUpperBound

public int getSupportUpperBound()
Gets the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1), i.e. $$\inf \{ x \in \mathbb Z : P(X \le x) = 1 \}$$. By convention, Integer.MAX_VALUE should be substituted for positive infinity.

The upper bound of the support is the number of trials except for the probability parameter p = 0.

Returns:
number of trials or 0.
• #### probability

public double probability(int x0,
int 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)

Special cases:

• returns 0.0 if x0 == x1;
• returns probability(x1) if x0 + 1 == x1;
Specified by:
probability in interface DiscreteDistribution
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 int inverseCumulativeProbability(double p)
Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is:

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

If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. In this case the result of cumulativeProbability(x) called using the returned p-quantile may not compute the original p.

The default implementation returns:

Specified by:
inverseCumulativeProbability in interface DiscreteDistribution
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 int 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:

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

If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. In this case the result of survivalProbability(x) called using the returned (1-p)-quantile may not compute the original p.

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 DiscreteDistribution
Parameters:
p - Cumulative probability.
Returns:
the smallest (1-p)-quantile of this distribution (largest 0-quantile for p = 1).
Throws:
IllegalArgumentException - if p < 0 or p > 1
• #### createSampler

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