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arXiv · 1301.7415

Learning Mixtures of DAG Models

Bo Thiesson·Christopher Meek·David Maxwell Chickering·David Heckerman
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Abstract

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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Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman. 2013-01-30. Learning Mixtures of DAG Models. https://arxiv.org/abs/1301.7415

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