[SciPy-user] Iterative proportional fitting

[Robert Kern]

On Thu, Jan 8, 2009 at 19:15, Dorian <wizzard028wise at gmail.com> wrote:
> Could you give me one appropriate  example on the way of adding the
> constraints?
>
> As a example In the case of given two marginal Gaussian distributions.
> I have written the corresponding bivariate Gaussian copula  density , after
> inverse transformation (using Sklar's theorem)  to get the joint density
> function  their is no correlation coefficient to infer on it.
> Because the joint density is not necessary a Gaussian density  and I stuck
> there .

Hmm, I could be talking out of my butt, here. The last time I looked
at something like this was years ago, and my problem was just
generating random numbers, not trying to derive density functions. I
was looking at the NORTA (NORmal To Anything) method. It might be
possible to derive a method for estimating a joint density using a
similar approach.

What information do you have? Just the marginal densities? Can you
describe your problem at a higher level?

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
  -- Umberto Eco