public final class MarsagliaTsangWangDiscreteSampler extends ObjectSampler 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
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
- See Also:
- Margsglia, et al (2004) JSS Vol. 11, Issue 3
Nested Class Summary
Nested Classes Modifier and Type Class Description
MarsagliaTsangWangDiscreteSampler.BinomialCreate a sampler for the Binomial distribution.
MarsagliaTsangWangDiscreteSampler.EnumeratedCreate a sampler for an enumerated distribution of
nvalues each with an associated probability.
MarsagliaTsangWangDiscreteSampler.PoissonCreate a sampler for the Poisson distribution.