Hypergeometric distribution

[Bengt Richter]

On Thu, 05 Jan 2006 09:47:02 -0600, Robert Kern <robert.kern at gmail.com> wrote:

>Bengt Richter wrote:
>> On 4 Jan 2006 12:46:47 -0800, "Raven" <balckraven at gmail.com> wrote:
>
>>>The problem with Stirling's approximation is that I need to calculate
>>>the hypergeometric hence the factorial for numbers within a large range
>>>e.g. choose(14000,170) or choose(5,2)
>> 
>> It seems you are hinting at some accuracy requirements that you haven't
>> yet explained. I'm curious how you use the values, and how that affects your
>> judgement of Stirling's approximation. In fact, perhaps the semantics of your
>> value usage could even suggest an alternate algorithmic approach to your actual end result.
>
>Does it matter? Implementing Stirling's approximation is pointless when
>scipy.special.gammaln() or scipy.special.gamma() does it for him.
>
Who's talking about implementing Stirling's approximation? ;-) I'm trying to determine first
why the OP is thinking there's a problem with using it at all. With "alternate algorithmic
approach" I didn't mean an alternate way of calculating Stirling's approximation. I meant
to allude to the possibility that pulling a little further on the requirements thread might
even unravel some of the rationale for calculating the hypergeometric per se, depending on
how he's actually using it and why. Same old, same old: requirements, requirements ;-)

Regards,
Bengt Richter