Class GeometricDistribution
- java.lang.Object
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- org.apache.commons.statistics.distribution.GeometricDistribution
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- All Implemented Interfaces:
DiscreteDistribution
public class GeometricDistribution extends java.lang.ObjectImplementation of the geometric distribution.
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Nested Class Summary
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Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.DiscreteDistribution
DiscreteDistribution.Sampler
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Constructor Summary
Constructors Constructor Description GeometricDistribution(double p)Creates a geometric distribution.
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Method Summary
Modifier and Type Method Description DiscreteDistribution.SamplercreateSampler(UniformRandomProvider rng)Creates a sampler.doublecumulativeProbability(int x)For a random variableXwhose values are distributed according to this distribution, this method returnsP(X <= x).doublegetMean()Gets the mean of this distribution.doublegetProbabilityOfSuccess()Access the probability of success for this distribution.intgetSupportLowerBound()Gets the lower bound of the support.intgetSupportUpperBound()Gets the upper bound of the support.doublegetVariance()Gets the variance of this distribution.intinverseCumulativeProbability(double p)Computes the quantile function of this distribution.booleanisSupportConnected()Indicates whether the support is connected, i.e.doublelogProbability(int x)For a random variableXwhose values are distributed according to this distribution, this method returnslog(P(X = x)), wherelogis the natural logarithm.doubleprobability(int x)For a random variableXwhose values are distributed according to this distribution, this method returnsP(X = x).doubleprobability(int x0, int x1)For a random variableXwhose values are distributed according to this distribution, this method returnsP(x0 < X <= x1).static int[]sample(int n, DiscreteDistribution.Sampler sampler)Utility function for allocating an array and filling it withnsamples generated by the givensampler.
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Method Detail
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getProbabilityOfSuccess
public double getProbabilityOfSuccess()
Access the probability of success for this distribution.- Returns:
- the probability of success.
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probability
public double probability(int x)
For a random variableXwhose values are distributed according to this distribution, this method returnsP(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.
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logProbability
public double logProbability(int x)
For a random variableXwhose values are distributed according to this distribution, this method returnslog(P(X = x)), wherelogis 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.
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cumulativeProbability
public double cumulativeProbability(int x)
For a random variableXwhose values are distributed according to this distribution, this method returnsP(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.
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getMean
public double getMean()
Gets the mean of this distribution. For probability parameterp, the mean is(1 - p) / p.- Returns:
- the mean, or
Double.NaNif it is not defined.
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getVariance
public double getVariance()
Gets the variance of this distribution. For probability parameterp, the variance is(1 - p) / (p * p).- Returns:
- the variance, or
Double.NaNif it is not defined.
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getSupportLowerBound
public int getSupportLowerBound()
Gets the lower bound of the support. This method must return the same value asinverseCumulativeProbability(0), i.e.inf {x in Z | P(X <= x) > 0}. By convention,Integer.MIN_VALUEshould be substituted for negative infinity. The lower bound of the support is always 0.- Returns:
- lower bound of the support (always 0)
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getSupportUpperBound
public int getSupportUpperBound()
Gets the upper bound of the support. This method must return the same value asinverseCumulativeProbability(1), i.e.inf {x in R | P(X <= x) = 1}. By convention,Integer.MAX_VALUEshould be substituted for positive infinity. The upper bound of the support is infinite (which we approximate asInteger.MAX_VALUE).- Returns:
- upper bound of the support (always Integer.MAX_VALUE)
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isSupportConnected
public boolean isSupportConnected()
Indicates whether the support is connected, i.e. whether all integers 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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inverseCumulativeProbability
public int inverseCumulativeProbability(double p)
Computes the quantile function of this distribution. For a random variableXdistributed according to this distribution, the returned value isinf{x in Z | P(X<=x) >= p}for0 < p <= 1,inf{x in Z | P(X<=x) > 0}forp = 0.
int, thenInteger.MIN_VALUEorInteger.MAX_VALUEis returned. The default implementation returnsDiscreteDistribution.getSupportLowerBound()forp = 0,DiscreteDistribution.getSupportUpperBound()forp = 1, andsolveInverseCumulativeProbability(double, int, int)for0 < p < 1.
- Specified by:
inverseCumulativeProbabilityin interfaceDiscreteDistribution- Parameters:
p- Cumulative probability.- Returns:
- the smallest
p-quantile of this distribution (largest 0-quantile forp = 0).
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probability
public double probability(int x0, int x1)For a random variableXwhose values are distributed according to this distribution, this method returnsP(x0 < X <= x1). The default implementation uses the identityP(x0 < X <= x1) = P(X <= x1) - P(X <= x0)- Specified by:
probabilityin interfaceDiscreteDistribution- Parameters:
x0- Lower bound (exclusive).x1- Upper bound (inclusive).- Returns:
- the probability that a random variable with this distribution
will take a value between
x0andx1, excluding the lower and including the upper endpoint.
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sample
public static int[] sample(int n, DiscreteDistribution.Sampler sampler)Utility function for allocating an array and filling it withnsamples generated by the givensampler.- Parameters:
n- Number of samples.sampler- Sampler.- Returns:
- an array of size
n.
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createSampler
public DiscreteDistribution.Sampler createSampler(UniformRandomProvider rng)
Creates a sampler.- Specified by:
createSamplerin interfaceDiscreteDistribution- Parameters:
rng- Generator of uniformly distributed numbers.- Returns:
- a sampler that produces random numbers according this distribution.
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