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Learning Mixtures of DAG Models
Bo Thiesson, Christopher Meek, David Chickering, David Heckerman
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.
Keywords:
Pages: 504-513
PS Link: ftp://ftp.research.microsoft.com/pub/TR/tr-97-30.ps
PDF Link: /papers/98/p504-thiesson.pdf
BibTex:
@INPROCEEDINGS{Thiesson98,
AUTHOR = "Bo Thiesson
and Christopher Meek and David Chickering and David Heckerman",
TITLE = "Learning Mixtures of DAG Models",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "504--513"
}
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