Package org.apache.commons.rng.sampling.distribution

Class KempSmallMeanPoissonSampler

java.lang.Object

org.apache.commons.rng.sampling.distribution.KempSmallMeanPoissonSampler

All Implemented Interfaces:

DiscreteSampler, SharedStateDiscreteSampler, SharedStateSampler<SharedStateDiscreteSampler>

public final class KempSmallMeanPoissonSampler
extends Object
implements SharedStateDiscreteSampler

Sampler for the Poisson distribution.

• Kemp, A, W, (1981) Efficient Generation of Logarithmically Distributed Pseudo-Random Variables. Journal of the Royal Statistical Society. Vol. 30, No. 3, pp. 249-253.

This sampler is suitable for mean < 40. For large means, LargeMeanPoissonSampler should be used instead.

Note: The algorithm uses a recurrence relation to compute the Poisson probability and a rolling summation for the cumulative probability. When the mean is large the initial probability (Math.exp(-mean)) is zero and an exception is raised by the constructor.

Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double.

Since:

1.3

See Also:

• Kemp, A.W. (1981) JRSS Vol. 30, pp. 249-253

Field Summary

Fields

Modifier and Type	Field	Description
private final double	mean	The mean of the Poisson sample.
private final double	p0	Pre-compute Math.exp(-mean).
private final UniformRandomProvider	rng	Underlying source of randomness.

Constructor Summary

Constructors

Modifier	Constructor	Description
private	KempSmallMeanPoissonSampler(UniformRandomProvider rng, double p0, double mean)

Method Summary

Modifier and Type	Method	Description
static SharedStateDiscreteSampler	of(UniformRandomProvider rng, double mean)	Creates a new sampler for the Poisson distribution.
int	sample()	Creates an int sample.
String	toString()
SharedStateDiscreteSampler	withUniformRandomProvider(UniformRandomProvider rng)	Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.commons.rng.sampling.distribution.DiscreteSampler

samples, samples

Field Details

rng

private final UniformRandomProvider rng

Underlying source of randomness.

p0

private final double p0

Pre-compute Math.exp(-mean). Note: This is the probability of the Poisson sample p(x=0).

mean

private final double mean

The mean of the Poisson sample.

Constructor Details

KempSmallMeanPoissonSampler

private KempSmallMeanPoissonSampler(UniformRandomProvider rng,
 double p0,
 double mean)

Parameters:

rng - Generator of uniformly distributed random numbers.

p0 - Probability of the Poisson sample p(x=0).

mean - Mean.

Method Details

sample

public int sample()

Creates an int sample.

Specified by:

sample in interface DiscreteSampler

Returns:

a sample.

toString

public String toString()

Overrides:

toString in class Object

withUniformRandomProvider

public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng)

Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.

Specified by:

withUniformRandomProvider in interface SharedStateSampler<SharedStateDiscreteSampler>

Parameters:

rng - Generator of uniformly distributed random numbers.

Returns:

the sampler

of

public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
 double mean)

Creates a new sampler for the Poisson distribution.

Parameters:

rng - Generator of uniformly distributed random numbers.

mean - Mean of the distribution.

Returns:

the sampler

Throws:

IllegalArgumentException - if mean <= 0 or Math.exp(-mean) == 0.