This algorithm reconstructs Bayesian Networks from observational data. Therefor it first builds the skeleton of the DAG (directed acyclic graph) with the max-min parents and children (mmpc) algorithm. Afterwards it directs the edges between the vertices with the Bayesian Dirichlet likelihood-equivalence uniform (BDeu) score. For more information on that read the report appended or "The max-min hill-climbing Bayesian network structure learning algorithm", by Ioannis Tsamardinos, Laura E. Brown & Constantin F. Aliferis.
|Maintainer||Michael Bauer <[email protected]>|
|Package repository||View on GitHub|
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