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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: [math] Generate random data using the Inverse CDF Method?
Date Tue, 27 Oct 2009 01:49:48 GMT
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 <phil.steitz@gmail.com> 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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