Re: [Pymc-user] Correlation between parameters

[Chris F. <fon...@gm...>]

On 2/15/06, David Huard <dav...@et...> wrote:
>  I have a model with two parameters A and B. When I compute their
> distribution using PyMC, I get a 'cigar' distribution, in other words,
> parameters A and B are strongly correlated. I figure this must put a stra=
in
> on the routine, forcing it to decrease the jump radius to maintain an
> acceptable acceptance ratio.  Is there a way to indicate that those
> parameters are correlated and to use a multivariate distribution for the
> jumps ?
>

Sorry for the late reply. The best way to deal with highly correlated
parameters is to block update -- i.e. sample them together. Thus,
rather than have A and B, you can just specify a vector-valued
parameter A with init_val =3D [x, y]. Of course, this assumes A and B
both use the same likelihood function. Is this the case? I should
probably add the ability to block update among arbitrary parameters.

>  P.S. Adding the generalized extreme distribution to flib would sure be
> appreciated.

Look for it in the next release.

C.

--
Chris Fonnesbeck + Atlanta, GA + http://trichech.us