MarsagliaTsangWangDiscreteSampler (Apache Commons RNG Sampling 1.6 API)

java.lang.Object
- org.apache.commons.rng.sampling.distribution.MarsagliaTsangWangDiscreteSampler

```
public final class MarsagliaTsangWangDiscreteSampler
extends Object
```
Sampler for a discrete distribution using an optimised look-up table.
- The method requires 30-bit integer probabilities that sum to 2³⁰ as described in George Marsaglia, Wai Wan Tsang, Jingbo Wang (2004) Fast Generation of Discrete Random Variables. Journal of Statistical Software. Vol. 11, Issue. 3, pp. 1-11.
Sampling uses 1 call to UniformRandomProvider.nextInt().

Memory requirements depend on the maximum number of possible sample values, n, and the values for the probabilities. Storage is optimised for n. The worst case scenario is a uniform distribution of the maximum sample size. This is capped at 0.06MB for n <= 2⁸, 17.0MB for n <= 2¹⁶, and 4.3GB for n <= 2³⁰. Realistic requirements will be in the kB range.

The sampler supports the following distributions:
- Enumerated distribution (probabilities must be provided for each sample)
- Poisson distribution up to mean = 1024
- Binomial distribution up to trials = 65535
Since:

1.3

See Also:

Margsglia, et al (2004) JSS Vol. 11, Issue 3

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`static class`	`MarsagliaTsangWangDiscreteSampler.Binomial`	Create a sampler for the Binomial distribution.
`static class`	`MarsagliaTsangWangDiscreteSampler.Enumerated`	Create a sampler for an enumerated distribution of `n` values each with an associated probability.
`static class`	`MarsagliaTsangWangDiscreteSampler.Poisson`	Create a sampler for the Poisson distribution.

Method Summary
- Methods inherited from class java.lang.Object
  clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait