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

chris at fisher.forestry.uga.edu chris at fisher.forestry.uga.edu
Fri Sep 24 19:33:17 CDT 2004


On 9/24/2004, "Charles Harris" <charles.harris at sdl.usu.edu> wrote:

>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.
>>
>>Cheers,
>>Chris Fonnesbeck
>>
>>chris (at) fisher.forestry.uga.edu
>>
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>>
>>
>>
>Chris,
>
>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?
>
>Chuck
>

The performance is good, relative to extant packages such as the BUGS
project. For the MCMC sampler, the bottleneck is in the repeated
evaluation of statistical likelihoods; scipy's are too slow, so I use
some f2py modules there; for the reinforcement learning stuff, the
innermost loop is coded inline with weave.

Chris




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