Bounding the False Discovery Rate in Local Bayesian Network Learning

Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development, variable selection, and causal discovery. The quality of the models, however, has often only been empirically evaluated; the available theoretical results typically guarantee asymptotic correctness (consistency) of the algorithms. This paper describes theoretical bounds on the quality of a fundamental Bayesian Network local-learning task in the finite sample using theories for controlling the False Discovery Rate. The behavior of the derived bounds is investigated across various problem and algorithm parameters. Empirical results support the theory which has immediate ramifications in the design of new algorithms for Bayesian Network learning, variable selection and causal discovery.

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Ioannis Tsamardinos, Laura E. Brown
• Keywords: AAAI 2008, Algorithms, Bayesian Network, Causal Discovery, Intelligent Agents