[SciPy-dev] stats.distributions.poisson loc parameter : is it wise ?
Thu Aug 6 16:49:22 CDT 2009
On Thu, Aug 6, 2009 at 16:43, Pierre GM<firstname.lastname@example.org> 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.
"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
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