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From Mikkel Meyer Andersen <m...@mikl.dk>
Subject Re: [math] Generate random data using the Inverse CDF Method?
Date Tue, 27 Oct 2009 00:33:34 GMT
Ted: No, I mean with the discrete inverse cdf. But anyway. Thanks for
clarifying the points.

Phil, if you're not convinced, I'll be happy to provide a
patch-draft/prototype of code so you can see exactly what I mean?

If we were to put a generator in the distributions (for supporting the
specialised generators), should this method then just be parameterised
by a RandomGenerator? Or what would be a proper approach?

2009/10/27 Ted Dunning <ted.dunning@gmail.com>:
> That was Phil. (not that it matters)
>
> +1 for the idea of a default generator for all distributions that define a
> cumulative density.
>
> +1 as well for specialized implementations where possible that over-ride the
> default generator even if it exists.
>
> I can't imagine much dispute on either of these points because they satisfy
> the general principle of doing the best we can for all cases as well as for
> special cases.
>
> I also completely agree with Mikkel with not understanding why the
> generation of deviates is separated from the distribution.
>
> On Mon, Oct 26, 2009 at 5:11 PM, Mikkel Meyer Andersen <mikl@mikl.dk> wrote:
>
>> Ted, sorry hadn't seen your e-mail before sending mine.
>>
>> Yes, I agree in you point of having specialised good algorithms. But
>> in lack of such methods, I'd prefer being able to have a general
>> method, although it might be bad compared to a specialised one.
>>
>> 2009/10/27 Phil Steitz <phil.steitz@gmail.com>:
>> > Thanks.  That's what I was missing. I would still rather see the
>> > implementations in the random package and for common distributions,
>> > e.g. Poisson, pick a method that is well-suited for the distribution.
>>
>

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