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From Mikkel Meyer Andersen <>
Subject Re: [math] Generate random data using the Inverse CDF Method?
Date Tue, 27 Oct 2009 02:01:14 GMT
As already stated, I agree with Ted. I think we should have a
nextSample() on the distributions. I'm also concerned that this
creates a need for another part of the library (RNG), but it _is_
already in the library, just like using erf- og gamma-functions. What
about making the RNGImp a thread-safe singleton and using this in the

And the nextSample() can be implemented in an abstract class providing
inverse cdf, and distributions with better sample generators just
override this?

2009/10/27 Ted Dunning <>:
> Are you against adding any nextSample() method to distributions at all
> (regardless of the quality of the implementation)?
> Or just unhappy about adding nextSample() hooked to a bad implementation?
> The first opinion, I just don't understand.  The second can be dealt with by
> putting in good implementations or by throwing UOE.
> I have a little bit of sympathy as a developer for separating all sampling
> from the distributions, but I have no sympathy at all with this as a user.
> I think of a distribution as something that you can take the density of,
> (often) get the cumulative distribution from and get a sample from.  I know
> in my heart of hearts that there is something down deep that is probably
> called a <mumble>DistributionGenerator.  I even know that underneath that,
> there is likely to be a uniform distribution generator.  What what I think
> about when using a system is "sampling from a distribution" just like
> anybody trained in statistics would.  That means that I expect
> <mumble>Distribution.nextSample() to exist.  I know that it might be fast or
> slow, but having hunted up the distribution I want, I *don't* want to have
> to imagine what class might generate the distribution I want.
> The key here is what a user of the system thinks.  Not how an implementor
> thinks.
> On Mon, Oct 26, 2009 at 6:01 PM, Phil Steitz <> wrote:
>> What I
>> am -1 on is adding (potentially poor) random data generation to the
>> distributions implementations.
> --
> Ted Dunning, CTO
> DeepDyve

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