Class PoissonSampler
- java.lang.Object
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- org.apache.commons.rng.sampling.distribution.SamplerBase
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- org.apache.commons.rng.sampling.distribution.PoissonSampler
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- 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 by1000 * 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
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Constructor Summary
Constructors Constructor Description PoissonSampler(UniformRandomProvider rng, double mean)This instance delegates sampling.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static SharedStateDiscreteSamplerof(UniformRandomProvider rng, double mean)Creates a new Poisson distribution sampler.intsample()Creates anintsample.StringtoString()SharedStateDiscreteSamplerwithUniformRandomProvider(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
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.apache.commons.rng.sampling.distribution.DiscreteSampler
samples, samples
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Constructor Detail
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PoissonSampler
public PoissonSampler(UniformRandomProvider rng, double mean)
This instance delegates sampling. Use the factory methodof(UniformRandomProvider, double)to create an optimal sampler.- Parameters:
rng- Generator of uniformly distributed random numbers.mean- Mean.- Throws:
IllegalArgumentException- ifmean <= 0ormean > 0.5 *Integer.MAX_VALUE.
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Method Detail
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sample
public int sample()
Creates anintsample.- Specified by:
samplein interfaceDiscreteSampler- Returns:
- a sample.
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toString
public String toString()
- Overrides:
toStringin classSamplerBase
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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:
withUniformRandomProviderin interfaceSharedStateSampler<SharedStateDiscreteSampler>- Parameters:
rng- Generator of uniformly distributed random numbers.- Returns:
- the sampler
- Since:
- 1.3
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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- ifmean <= 0ormean > 0.5 *Integer.MAX_VALUE.- Since:
- 1.3
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