[SciPy-User] Fitting procedure to take advantage of cluster

[Giovanni Luca Ciampaglia]

Hi,
there are several strategies, depending on your problem. You could use a 
surrogate model, like a Gaussian Process, to fit the data (see for 
example Higdon et al 
http://epubs.siam.org/sisc/resource/1/sjoce3/v26/i2/p448_s1?isAuthorized=no). 
I have personally used scikits.learn for GP estimation but there is also 
PyMC that should do the same (never tried it).

Another option could be indirect inference, but if each run of your 
model takes several minutes to compute probably it's not the best option:
http://cscs.umich.edu/~crshalizi/notabene/indirect-inference.html

HTH

Giovanni

Il 29. 06. 11 18:54, J. David Lee ha scritto:
> Hello,
>
> I'm attempting to perform a fit of a model function's output to some
> measured data. The model has around 12 parameters, and takes tens of
> minutes to run. I have access to a cluster with several thousand
> processors that can run the simulations in parallel, so I'm wondering if
> there are any algorithms out there that I can use to leverage this
> computing power to efficiently solve my problem - that is, besides grid
> searches or Monte-Carlo methods.
>
> Thanks for your help,
>
> David
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-- 
Giovanni Luca Ciampaglia

Ph.D. Candidate
Faculty of Informatics
University of Lugano
Web: http://www.inf.usi.ch/phd/ciampaglia/

Bertastraße 36 ? 8003 Zürich ? Switzerland

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