Learning mixtures of DAG models

We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple search-and-score algorithms are infeasible for a variety of problems, and introduce a feasible approach in which parameter and structure search is interleaved and expected data is treated as real data. Our approach can be viewed as a combination of (1) the Cheeseman-Stutz asymptotic approximation for model posterior probability and (2) the Expectation-Maximization algorithm. We evaluate our procedure for selecting among MDAGs on synthetic and real examples.

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In  Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence

Publisher  Morgan Kaufmann Publishers
All copyrights reserved by Morgan Kaufmann Publishers 2007.


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