Supply nextSample for all distributions with inverse cdf using inverse
transform sampling approach
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Key: MATH-310
URL: https://issues.apache.org/jira/browse/MATH-310
Project: Commons Math
Issue Type: Improvement
Affects Versions: 2.0
Reporter: Mikkel Meyer Andersen
Priority: Minor
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
nextSample-approaches 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 inverseCumulativeProbability-function.
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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