---------- Forwarded message ----------
From: Jervis Whitley <jervi...@gmail.com>
Date: Wed, Jan 14, 2009 at 9:26 AM
Subject: Re: [Tutor] Gamma distribution function
To: culpritNr1 <ig2ar-s...@yahoo.co.uk>
On Wed, Jan 14, 2009 at 9:11 AM, culpritNr1 <ig2ar-s...@yahoo.co.uk> wrote:
>
> The python documentation on this functionality is extremely poor. Look
> >>> help("scipy.stats.distributions.poisson.rvs")
> Help on method rvs in scipy.stats.distributions.poisson:
> scipy.stats.distributions.poisson.rvs = rvs(self, *args, **kwds) method of
> scipy.stats.distributions.poisson_gen instance
>
> Do you understand what's going on?
>
> Thanks,
>
> culpritNr1
>
>
> --
> View this message in context:
> http://www.nabble.com/Gamma-distribution-function-tp21444899p21445597.html
> Sent from the Python - tutor mailing list archive at Nabble.com.
>
> _______________________________________________
> Tutor maillist - Tutor@python.org
> http://mail.python.org/mailman/listinfo/tutor
>
Your previous email said you wanted to sample from the poisson function,
which is what that rvs method is doing, taking random samples from a poisson
distribution of lambda = 1 in your case. They also provide a means to shift
the function from a nominal x crossing of 0 using the second argument, in
your case you have used 2. So you were sampling from a function of mean 1
shifted right by 2.
The below has been taken from the poisson documentation itself:
print stats.distributions.poisson.__doc__
A poisson discrete random variable.
Discrete random variables are defined from a standard form.
The standard form may require some other parameters to complete
its specification. The distribution methods also take an optional
location
parameter using loc= keyword. The default is loc=0. The calling form
of the methods follow:
poisson.rvs(mu,loc=0)
- random variates
poisson.pmf(x,mu,loc=0)
- probability mass function
poisson.cdf(x,mu,loc=0)
- cumulative density function
poisson.sf(x,mu,loc=0)
- survival function (1-cdf --- sometimes more accurate)
poisson.ppf(q,mu,loc=0)
- percent point function (inverse of cdf --- percentiles)
poisson.isf(q,mu,loc=0)
- inverse survival function (inverse of sf)
poisson.stats(mu,loc=0,moments='mv')
- mean('m',axis=0), variance('v'), skew('s'), and/or kurtosis('k')
poisson.entropy(mu,loc=0)
- entropy of the RV
Alternatively, the object may be called (as a function) to fix
the shape and location parameters returning a
"frozen" discrete RV object:
myrv = poisson(mu,loc=0)
- frozen RV object with the same methods but holding the
given shape and location fixed.
You can construct an aribtrary discrete rv where P{X=xk} = pk
by passing to the rv_discrete initialization method (through the values=
keyword) a tuple of sequences (xk,pk) which describes only those values
of
X (xk) that occur with nonzero probability (pk).
Poisson distribution
poisson.pmf(k, mu) = exp(-mu) * mu**k / k!
for k >= 0
If you are after a probability at a given k (which it now sounds like you
may be after) you might be interested in the
pmf method.
(Sorry I did a reply instead of reply-all)
Cheers,
Jervis
_______________________________________________
Tutor maillist - Tutor@python.org
http://mail.python.org/mailman/listinfo/tutor
