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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. 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`.)