Learning Mixtures of DAG Models
Machine Learning
2015-05-19 v2 Artificial Intelligence
Machine Learning
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
Cite
@article{arxiv.1301.7415,
title = {Learning Mixtures of DAG Models},
author = {Bo Thiesson and Christopher Meek and David Maxwell Chickering and David Heckerman},
journal= {arXiv preprint arXiv:1301.7415},
year = {2015}
}
Comments
Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)