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Gilles commented on MATH310:

Wouldn't something along the following lines satisfy everybody's concerns?
In each "...Distribution" interface, add a "nextSample()" (with the appropriate return type).
In each "...DistributionImpl", add a "RandomData" instance variable to be used within the
"nextSample()" implementation. E.g. in "PoissonDistributionImpl.java"
CUT
import org.apache.commons.math.random.RandomData;
import org.apache.commons.math.random.RandomDataImpl;
public class PoissonDistributionImpl extends ... {
private final RandomData randomData = new RandomDataImpl();
// ....
long nextSample() {
randomData.nextPoisson();
}
}
CUT
> Supply nextSample for all distributions with inverse cdf using inverse transform sampling
approach
> 
>
> Key: MATH310
> URL: https://issues.apache.org/jira/browse/MATH310
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 2.0
> Reporter: Mikkel Meyer Andersen
> Priority: Minor
> Attachments: patch_proposal
>
> Original Estimate: 3h
> Remaining Estimate: 3h
>
> To be able to generate samples from the supported probability distributions, a generic
function nextSample is implemented in AbstractContinuousDistribution and AbstractIntegerDistribution.
This also gives the possibility to override the method if better algorithms are available
for specific distributions as shown in the small example with the exponential distribution.
> Because the nextExponential is used several places: in nextPoisson it can be replaces
by an instance if the ExponentialDistribution and in ValueServer it can as well, although
maybe not in as natural maner as the other.
> This problem with the Exponential is a special problem. In general the nextSampleapproaches
immediately gives the possibility the sample from all the distributions with inverse cdf instead
just only a couple.
> Only AbstractContinuousDistribution and AbstractIntegerDistribution extends AbstractDistribution,
and both AbstractIntegerDistribution and AbstractContinuousDistribution has an inverseCumulativeProbabilityfunction.
But in AbstractContinuousDistribution the inverse cdf returns a double, and at AbstractIntegerDistribution
it  naturally  returns an integer. Therefor the nextSample is not put on AbstractDistribution,
but on each extension with different return types.
> RandomGenerator as parameter instead of getting a RNG inside the nextSample, because
one typically wants to use the same RNG because often several random samples are wanted. Another
option is to have a RNG as a field in the class, but that would be more ugly and also result
in several RNGs at runtime.
> The nextPoisson etc. ought to be moved as well, if the enhancement is accepted, but it
should be a quick fix.
> Tests has to be written for this change as well.

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