Bo Thiesson, Christopher Meek, David Maxwell Chickering, and David Heckerman
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.
In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence
Publisher Morgan Kaufmann Publishers
All copyrights reserved by Morgan Kaufmann Publishers 2007.