On Wed, Sep 2, 2009 at 2:15 PM, Raymond Hettinger wrote:
> ISTM, there ought to be a statistics module that can calculate
> cumulative distribution functions for a variety of distributions.
> This would be far more helpful than creating more generators.
>
Many of the formulas for cumulative dist
Robert Kern wrote:
On 2009-09-02 14:15 PM, Raymond Hettinger wrote:
On Sep 2, 6:51 am, Thomas Philips wrote:
While the random module allows one to generate randome numbers with a
variety of distributions, some useful distributions are omitted - the
Student's t being among them.
I'm curious t
On 2009-09-02 14:15 PM, Raymond Hettinger wrote:
On Sep 2, 6:51 am, Thomas Philips wrote:
While the random module allows one to generate randome numbers with a
variety of distributions, some useful distributions are omitted - the
Student's t being among them.
I'm curious to hear what your use
On Sep 2, 2:37 pm, Mark Dickinson wrote:
> On Sep 2, 6:15 pm, Thomas Philips wrote:
>
> > I mis-spoke - the variance is infinite when df=2 (the variance is df/
> > (df-2),
>
> Yes: the variance is infinite both for df=2 and df=1, and Student's t
> with df=1 doesn't even have an expectation. I d
> To get this into core Python, you'd usually submit a feature request
> athttp://bugs.python.org.
If you do submit a patch, please assign it to me.
I've been the primary maintainer for that module
for several years.
Raymond Hettinger
--
http://mail.python.org/mailman/listinfo/python-list
On Sep 2, 6:51 am, Thomas Philips wrote:
> While the random module allows one to generate randome numbers with a
> variety of distributions, some useful distributions are omitted - the
> Student's t being among them.
I'm curious to hear what your use cases are.
My understanding is that t-distrib
On Sep 2, 6:15 pm, Thomas Philips wrote:
> I mis-spoke - the variance is infinite when df=2 (the variance is df/
> (df-2),
Yes: the variance is infinite both for df=2 and df=1, and Student's t
with df=1 doesn't even have an expectation. I don't see why this
would stop you from generating meanin
On 2009-09-02 11:28 AM, Mark Dickinson wrote:
On Sep 2, 2:51 pm, Thomas Philips wrote:
def student_t(df): # df is the number of degrees of freedom
if df< 2 or int(df) != df:
raise ValueError, 'student_tvariate: df must be a integer> 1'
By the way, why do you exclude th
On Sep 2, 1:03 pm, Thomas Philips wrote:
> On Sep 2, 12:28 pm, Mark Dickinson wrote:
>
> > On Sep 2, 2:51 pm, Thomas Philips wrote:
>
> > > def student_t(df): # df is the number of degrees of freedom
> > > if df < 2 or int(df) != df:
> > > raise ValueError, 'student_tvariate:
On Sep 2, 12:28 pm, Mark Dickinson wrote:
> On Sep 2, 2:51 pm, Thomas Philips wrote:
>
> > def student_t(df): # df is the number of degrees of freedom
> > if df < 2 or int(df) != df:
> > raise ValueError, 'student_tvariate: df must be a integer > 1'
>
> By the way, why do you
On Sep 2, 2:51 pm, Thomas Philips wrote:
> def student_t(df): # df is the number of degrees of freedom
> if df < 2 or int(df) != df:
> raise ValueError, 'student_tvariate: df must be a integer > 1'
By the way, why do you exclude the possibility df=1 here?
--
Mark
--
http://m
On Sep 2, 2:51 pm, Thomas Philips wrote:
> While the random module allows one to generate randome numbers with a
> variety of distributions, some useful distributions are omitted - the
> Student's t being among them. This distribution is easily derived from
> the normal distribution and the chi-sq
While the random module allows one to generate randome numbers with a
variety of distributions, some useful distributions are omitted - the
Student's t being among them. This distribution is easily derived from
the normal distribution and the chi-squared distribution (which in
turn is a special cas
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