2011/9/19 Brian Holt <[email protected]>: > Does [Bayesian Inference](http://en.wikipedia.org/wiki/Bayesian_inference) > fall under the scope of scikit-learn? Probabilistic graphical models > are an exciting field in machine learning, with the theory going back > at least as far as 1982.
Given that much of sklearn is bound together by common interfaces (fit/predict/transform), can you draft an interface to Bayesian networks that is Pythonic, easy to use, and fits in with the rest? I know some Bayes nets packages go so far as to offer a DSL for them instead of a library, which to suggests that designing interfaces to them may be difficult... > [InferNet](http://research.microsoft.com/en-us/um/cambridge/projects/infernet/) > has IronPython bindings, but is probably not suitable for wrapping in > scikit-learn. [libDAI](http://cs.ru.nl/~jorism/libDAI/doc/) seems to > be a possibility for wrapping in much the same way as libSVM. Never tried any of these, but "arbitrary factor graphs with discrete variables" including MRFs and Bayes nets sounds interesting. Did you ever try this libDAI? It seems to have most of the stuff that ML-oriented NLP researchers will want to have. Regards, -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ BlackBerry® DevCon Americas, Oct. 18-20, San Francisco, CA Learn about the latest advances in developing for the BlackBerry® mobile platform with sessions, labs & more. See new tools and technologies. Register for BlackBerry® DevCon today! http://p.sf.net/sfu/rim-devcon-copy1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
