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, wait
samples, samples
public SmallMeanPoissonSampler(UniformRandomProvider rng, double mean)
rng
- Generator of uniformly distributed random numbers.mean
- Mean.IllegalArgumentException
- if mean <= 0
or Math.exp(-mean) == 0
public int sample()
int
sample.sample
in interface DiscreteSampler
public 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
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