[SciPy-dev] MCMC, Kalman Filtering, AI for SciPy?

Charles Harris charles.harris at sdl.usu.edu
Fri Sep 24 15:09:22 CDT 2004

chris at fisher.forestry.uga.edu wrote:

>For the past couple of years, I have been maintaining a package called
>PyMC (http://pymc.sourceforge.net) which provides a general Markov chain
>Monte Carlo sampler (Metropolis-Hastings), linear and non-linear Kalman
>filtering algorithms and several reinforcement learning algorithms for
>AI applications. To date, it has been used mostly for ecological
>modeling applications, but is far more general than that. This package
>currently relies on SciPy, particularly for such tasks as plotting, and
>uses f2py extensively.
>I am wondering if there is interest in integrating such functionality
>into SciPy. If so, I would be happy to contribute, integrate and
>maintain the code under the auspices of the SciPy project. If not, I am
>equally content to continue maintaining PyMC separately. I encourage
>those interested to check out PyMC and let me know if it would be
>worthwhile to add any of this to SciPy.
>Chris Fonnesbeck
>chris (at) fisher.forestry.uga.edu
>Scipy-dev mailing list
>Scipy-dev at scipy.net

I'm definitely interested. The problem I always saw with the Montecarlo 
type samplers was that the core routine that I wanted to
write in Python was always called in the innermost loop, so the sampling 
was slow. Are your routines pure python, or are they
C code, and how do you get good performance?


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