On Mon, 26 Sep 2016 21:22:35 -0500, Brent Worden wrote:
First candidates are:
* Non-uniform deviates (i.e. the samplers now defined in
Commons Math's "o.a.c.math4.distribution" package),
I agree this doesn't belong to commons-rng, but I'm not convinced
it
would fit a commons-rng-tools component. Maybe a component more
targeted
toward statistic algorithms?
Sampling and statistics do not necessarily belong together.
[This was a discussion in CM.]
+1
Having used commons-math to generate random deviates, the
commons-math
approach of coupling random deviate generation to the distributions
themselves proved to be a bad development experience as well as not
the
most performant in the form of object instantiations.
Are you referring to the code in the 3.x line (branch "MATH_3_X"), or
in
the development branch (branch "master"[1]).
In unreleased CM4, the sampling is still coupled with the distribution
class but a RNG is passed only when a sampler is created.
Specialized
random variate generators would be a lot better design IMO.
If what's in CM4 is still not satisfactory, could you provide more
details on a better design?
Regards,
Gilles
[1] "Current" development branch was in branch "develop" until last
week.
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