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 SamplerBase

nextDouble, nextInt, nextInt, nextLong

Methods inherited from class Object

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

Methods inherited from interface DiscreteSampler

samples, samples

Constructor Details

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 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 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