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

Class SmallMeanPoissonSampler

java.lang.Object

org.apache.commons.rng.sampling.distribution.SmallMeanPoissonSampler

All Implemented Interfaces:

DiscreteSampler, SharedStateDiscreteSampler, SharedStateSampler<SharedStateDiscreteSampler>

public class SmallMeanPoissonSampler
extends Object
implements SharedStateDiscreteSampler

Sampler for the Poisson distribution.

• For small means, a Poisson process is simulated using uniform deviates, as described in

  Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 Important integer-valued distributions: The Poisson distribution. Addison Wesley.

  The Poisson process (and hence, the returned value) is bounded by 1000 * mean.

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

Sampling uses UniformRandomProvider.nextDouble() and requires on average mean + 1 deviates per sample.

Since:

1.1

Constructor Summary

Constructors

Constructor	Description
SmallMeanPoissonSampler(UniformRandomProvider rng, double mean)	Create an instance.

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

Constructor Detail

SmallMeanPoissonSampler

public SmallMeanPoissonSampler(UniformRandomProvider rng,
                               double mean)

Create an instance.

Parameters:

rng - Generator of uniformly distributed random numbers.

mean - Mean.

Throws:

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

Method Detail

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

Since:

1.3

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.

Since:

1.3