[SciPy-user] Iterative proportional fitting

josef.pktd@gmai... josef.pktd@gmai...
Thu Jan 8 22:02:58 CST 2009

On Thu, Jan 8, 2009 at 10:04 PM, Dorian <wizzard028wise@gmail.com> wrote:

> Hi  Kern ,  James
> I look at closely the "Maximum entropy method " and "NORTA method" , they
> correspond exactly
> to what I'm looking for to start thinking deeply about the problem of
> approaching likely the density
> function which will  correspond to a given marginal densities functions.
> I was reading a bit during this thread, since besides copula, I haven't
heard of the other methods.

Dorian, you haven't mentioned what kind of data you have. From some quick
reading, it seems that iterative proportional fitting is often used for
contingency tables, copulas are used in finance, where the underlying
distribution is continuous and usually many observations are available.

The first few google searches for NORTA consider it as a normal copula with
discrete marginals. There is a maximum entropy estimation package in scipy
that I don't know much about, applications show up mostly for
ontologies/language (see scipy\maxentropy\examples)

So, I guess, the popularity of the approach depends on the field and data

In my search on copulas, I found a good description at
where they use Kendals tau to estimate the correlation parameter for the
normal copula (and also in other copulas). The Wikipedia article is
unfortunately silent on estimation.

Since the problem of generating multivariate distribution is pretty
widespread, it would be useful to add some recipes to the cookbook, or to
this thread. So, if your search produces some examples that you are willing
to share, I and, I guess, the next user with a similar question would
appreciate it.

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