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

• Type: Conference
• Year: 1998
• Where: UAI
• Authors: Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman
• Keywords: Acyclic Graphical Model, Model Posterior Probability, Simple Search-and-score Algorithms, UAI 1998, UAI 2008