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

Class PoissonSampler

java.lang.Object

org.apache.commons.rng.sampling.distribution.SamplerBase

org.apache.commons.rng.sampling.distribution.PoissonSampler

All Implemented Interfaces:

DiscreteSampler, SharedStateDiscreteSampler, SharedStateSampler<SharedStateDiscreteSampler>

public class PoissonSampler
extends SamplerBase
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.
• For large means, we use the rejection algorithm described in

  Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables
  Computing vol. 26 pp. 197-207.

Sampling uses:

• UniformRandomProvider.nextDouble()
• UniformRandomProvider.nextLong() (large means only)

Since:

1.0

Constructor Summary

Constructors

Constructor	Description
PoissonSampler(UniformRandomProvider rng, double mean)	This instance delegates sampling.

Method Summary

Modifier and Type	Method	Description
static SharedStateDiscreteSampler	of(UniformRandomProvider rng, double mean)	Creates a new Poisson distribution sampler.
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 org.apache.commons.rng.sampling.distribution.SamplerBase

nextDouble, nextInt, nextInt, nextLong

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

PoissonSampler

public PoissonSampler(UniformRandomProvider rng,
                      double mean)

This instance delegates sampling. Use the factory method of(UniformRandomProvider, double) to create an optimal sampler.

Parameters:

rng - Generator of uniformly distributed random numbers.

mean - Mean.

Throws:

IllegalArgumentException - if mean <= 0 or mean > 0.5 * Integer.MAX_VALUE.

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 SamplerBase

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 Poisson distribution sampler.

Parameters:

rng - Generator of uniformly distributed random numbers.

mean - Mean.

Returns:

the sampler

Throws:

IllegalArgumentException - if mean <= 0 or mean > 0.5 * Integer.MAX_VALUE.

Since:

1.3