[SciPy-dev] stats.distributions.poisson loc parameter : is it wise ?

[Robert Kern]

On Thu, Aug 6, 2009 at 16:43, Pierre GM<pgmdevlist at gmail.com> wrote:
>
> On Aug 6, 2009, at 5:34 PM, Robert Kern wrote:
>>
>> Every probability distribution can be generalized to accept a location
>> and scale parameter even if their standard treatments do not.
>>
>>  pdf(x; loc,scale) -> pdf((x-loc)/scale)/scale
>
> Agreed, as long as we are talking about *continuous* distributions.
> The behavior is quite different for *discrete* distributions. Even if
> the scale is simply discarded already, using a location will probably
> NOT give the expected result

It depends on what your expectations are. For the discrete
distributions, all the loc parameter means is this, as documented:

  pmf(x; loc) -> pmf(x-loc)

That's it. I don't know why you would expect anything else.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
  -- Umberto Eco