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Hello fellow Number crunchers
As the headline suggests, I am looking for a library for learning and inference of Bayesian Networks. I have already found some, but I am hoping for a recommendation.
Requirements in a quick overview:
Which one do you recommend ?
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The most significant disadvantage is that there is no universally acknowledged method for constructing networks from data. There have been many developments in this regard, but there hasn't been a conqueror in a long time. The design of Bayesian Networks is hard to make compared to other networks.
pgmpy [pgmpy] is a python library for working with graphical models. It al- lows the user to create their own graphical models and answer inference or map queries over them. pgmpy has implementation of many inference algorithms like VariableElimination, Belief Propagation etc.
The learning of Bayesian network classifiers from data is commonly performed in a supervised manner, meaning that a training set containing examples that have been previously classified by an expert are used to generate the directed acyclic graph (DAG) and its conditional probability table (CPT).
Have a look at Weka. It's kind of popular in my neck of the woods... It's open source and written in Java.
This will tell you about bayesian networks in Weka, from the abstract:
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