On Fri, May 11, 2012 at 2:04 AM, <josef.pktd@gmail.com> wrote:
Why do the discrete distributions not have a `fit` method like the continuous distributions?
currently it's a bug in the documentation http://projects.scipy.org/scipy/ticket/1659
in statsmodels, we fit several of the discrete distributions.
Which ones? And do you then return non-integer parameters or not?
How about discrete parameters? (in analogy to the erlang discussion)
hypergeom is based on a story about marbles or balls
http://en.wikipedia.org/wiki/Hypergeometric_distribution#Application_and_exa... but why should we care, it's just a discrete distribution with 3 shape parameters, isn't it?
fractional marbles ?
nn = np.linspace(4.5, 8, 101) pmf = [stats.hypergeom.pmf(5, 10.8, n, 8.5) for n in nn]
plt.plot(nn, pmf, '-o') plt.title("pmf of hypergeom as function of parameter n")
Doesn't look like there are any problems, and the likelihood function is nicely concave.
conclusion: scipy.stats doesn't have a hypergeometric distribution, but a generalized version that is defined on a real parameter space.
Josef (so what's the point? Sorry, I was just getting distracted while looking for `fit`.)
For functions that work with continuous input, perhaps using the continuous fit and then looking for the best-fit with integer params near the continuous optimum would work. I looked for literature on this topic, but didn't find anything useful yet. Ralf