
Learning Bayesian belief networks from data – in Beta - randomphysicist
https://github.com/att-innovate/belief_network_lib
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randomphysicist
Recently open sourced Python for library for Bayesian belief networks (aka
Bayesian networks, aka probabilistic graphical models).

These are a powerful tool for representing dependence relationships in
probability distributions. Given a joint probability distribution, Pr(X1, X2,
..., Xk), a table representing this requires |X1|X...X|Xk| entries. This
representation can be accomplished much more compactly by identifying
conditional independence relationships among the variables. Belief networks,
are one method of encoding these independence relationships. Once this network
is identified, it can then be used to perform various types of probabilistic
inference.

