public class BinomialDistribution extends AbstractIntegerDistribution
random, randomData| Constructor and Description | 
|---|
| BinomialDistribution(int trials,
                    double p)Create a binomial distribution with the given number of trials and
 probability of success. | 
| BinomialDistribution(RandomGenerator rng,
                    int trials,
                    double p)Creates a binomial distribution. | 
| Modifier and Type | Method and Description | 
|---|---|
| double | cumulativeProbability(int x)For a random variable  Xwhose values are distributed according
 to this distribution, this method returnsP(X <= x). | 
| int | getNumberOfTrials()Access the number of trials for this distribution. | 
| 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. | 
| double | getProbabilityOfSuccess()Access the probability of success for this distribution. | 
| int | getSupportLowerBound()Access the lower bound of the support. | 
| int | 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 | logProbability(int x)For a random variable  Xwhose values are distributed according to
 this distribution, this method returnslog(P(X = x)), wherelogis the natural logarithm. | 
| double | probability(int x)For a random variable  Xwhose values are distributed according
 to this distribution, this method returnsP(X = x). | 
cumulativeProbability, inverseCumulativeProbability, reseedRandomGenerator, sample, sample, solveInverseCumulativeProbabilitypublic BinomialDistribution(int trials,
                    double p)
 Note: this constructor will implicitly create an instance of
 Well19937c as random generator to be used for sampling only (see
 AbstractIntegerDistribution.sample() and AbstractIntegerDistribution.sample(int)). In case no sampling is
 needed for the created distribution, it is advised to pass null
 as random generator via the appropriate constructors to avoid the
 additional initialisation overhead.
trials - Number of trials.p - Probability of success.NotPositiveException - if trials < 0.OutOfRangeException - if p < 0 or p > 1.public BinomialDistribution(RandomGenerator rng, int trials, double p)
rng - Random number generator.trials - Number of trials.p - Probability of success.NotPositiveException - if trials < 0.OutOfRangeException - if p < 0 or p > 1.public int getNumberOfTrials()
public double getProbabilityOfSuccess()
public double probability(int x)
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.x - the point at which the PMF is evaluatedxpublic double logProbability(int x)
X whose values are distributed according to
 this distribution, this method returns log(P(X = x)), where
 log is the natural logarithm. In other words, this method
 represents the logarithm of the probability mass function (PMF) for the
 distribution. 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
 IntegerDistribution.probability(int).
 
 The default implementation simply computes the logarithm of probability(x).
logProbability in class AbstractIntegerDistributionx - the point at which the PMF is evaluatedxpublic double cumulativeProbability(int 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 evaluatedxpublic double getNumericalMean()
n trials and probability parameter p, the mean is
 n * p.Double.NaN if it is not definedpublic double getNumericalVariance()
n trials and probability parameter p, the variance is
 n * p * (1 - p).Double.POSITIVE_INFINITY or
 Double.NaN if it is not defined)public int getSupportLowerBound()
inverseCumulativeProbability(0). In other words, this
 method must return
 inf {x in Z | P(X <= x) > 0}.
p = 1.public int getSupportUpperBound()
inverseCumulativeProbability(1). In other words, this
 method must return
 inf {x in R | P(X <= x) = 1}.
p = 0.public boolean isSupportConnected()
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