public class SmallMeanPoissonSampler extends Object implements SharedStateDiscreteSampler
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.
| Constructor and Description |
|---|
SmallMeanPoissonSampler(UniformRandomProvider rng,
double mean) |
| Modifier and Type | Method and 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.
|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitsamples, samplespublic SmallMeanPoissonSampler(UniformRandomProvider rng, double mean)
rng - Generator of uniformly distributed random numbers.mean - Mean.IllegalArgumentException - if mean <= 0 or Math.exp(-mean) == 0public int sample()
int sample.sample in interface DiscreteSamplerpublic SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng)
withUniformRandomProvider in interface SharedStateSampler<SharedStateDiscreteSampler>rng - Generator of uniformly distributed random numbers.public static SharedStateDiscreteSampler of(UniformRandomProvider rng, double mean)
rng - Generator of uniformly distributed random numbers.mean - Mean of the distribution.IllegalArgumentException - if mean <= 0 or Math.exp(-mean) == 0.Copyright © 2016–2022 The Apache Software Foundation. All rights reserved.