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I use them, but there are plenty of libraries out there. http://compbio.cs.huji.ac.il/FastInf/fastInf/FastInf_Homepage.html http://compbio.cs.huji.ac.il/FastIn

by danger 16y ago

I use them, but there are plenty of libraries out there. http://compbio.cs.huji.ac.il/FastInf/fastInf/FastInf_Homepage.html http://compbio.cs.huji.ac.il/FastInf/fastInf/FastInf_Homepag... http://robotics.stanford.edu/~sgould/svl/ http://robotics.stanford.edu/~sgould/svl/ http://code.google.com/p/factorie/ http://code.google.com/p/factorie/ http://people.kyb.tuebingen.mpg.de/jorism/libDAI/ http://people.kyb.tuebingen.mpg.de/jorism/libDAI/ http://research.microsoft.com/en-us/um/cambridge/projects/infernet/ http://research.microsoft.com/en-us/um/cambridge/projects/in... BTW, factor graphs are generally more popular than Bayes Nets these days, at least in research circles. (Bayesian networks can be seen as a special case of factor graph.)

mitmatt 16y ago
Those are a lot of great links! But I disagree with your "BTW" line: I don't think factor graphs are more popular than Bayes nets (a.k.a. directed graphical models), at least not in general machine learning (though perhaps in some particular subfield?). Directed graphs are usually most appropriate for generative models, which are very popular, especially in Bayesian approaches. In graphical models, directed, undirected, and factor graphs are all used in their appropriate contexts. And it's not accurate to say that directed models (Bayes nets) are a special case of factor graphs: there are conditional independence structures that can be represented by Bayes nets that can't be captured exactly with factor graphs. The canonical example is O --> O <-- O
- danger 16y ago
  Glad you like the links, but I disagree with your disagreement =P You can use factor graphs for either directed or undirected models. See this paper: Extending factor graphs so as to unify directed and undirected graphical models B. J. Frey Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, 257-264, Morgan Kaufmann, San Francisco, CA, August 2003.

by danger 16y ago

mitmatt 16y ago

Those are a lot of great links! But I disagree with your "BTW" line: I don't think factor graphs are more popular than Bayes nets (a.k.a. directed graphical models), at least not in general machine learning (though perhaps in some particular subfield?). Directed graphs are usually most appropriate for generative models, which are very popular, especially in Bayesian approaches. In graphical models, directed, undirected, and factor graphs are all used in their appropriate contexts. And it's not accurate to say that directed models (Bayes nets) are a special case of factor graphs: there are conditional independence structures that can be represented by Bayes nets that can't be captured exactly with factor graphs. The canonical example is O --> O <-- O

danger 16y ago
Glad you like the links, but I disagree with your disagreement =P You can use factor graphs for either directed or undirected models. See this paper: Extending factor graphs so as to unify directed and undirected graphical models B. J. Frey Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, 257-264, Morgan Kaufmann, San Francisco, CA, August 2003.