public final class KempSmallMeanPoissonSampler extends Object implements SharedStateDiscreteSampler
This sampler is suitable for mean < 40
. For large means,
LargeMeanPoissonSampler
should be used instead.
Note: The algorithm uses a recurrence relation to compute the Poisson probability and a rolling summation for the cumulative probability. When the mean is large the initial probability (Math.exp(-mean)) is zero and an exception is raised by the constructor.
Sampling uses 1 call to UniformRandomProvider.nextDouble()
. This method provides
an alternative to the SmallMeanPoissonSampler
for slow generators of double
.
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 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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