Thank you David for phrasing well exactly my thoughts: * Bayesian inference != Bayes Net (Graphical models)
* To solve Bayesian problems efficiently with sampling requires quite an infrastructure, PyMC does this very well, let us not replicate this. In specific cases of Bayesian inference without sampling, it perfectly fits in the scope of the scikit, and we already have some code (mixture models, or Gaussian processes, for instance) * Graphical models: why not (I actually do some Gaussian graphical models research that, I hope, will benefit to the scikit one day), but as usual, ironing the code takes a while. My 2 cents, Gaël ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity and more. Splunk takes this data and makes sense of it. Business sense. IT sense. Common sense. http://p.sf.net/sfu/splunk-d2dcopy1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
