Advances in Artificial Intelligence

Volume 6085 of the series Lecture Notes in Computer Science pp 123-134

The IMAP Hybrid Method for Learning Gaussian Bayes Nets

  • Oliver SchulteAffiliated withSchool of Computing Science, Simon Fraser University
  • , Gustavo FrigoAffiliated withSchool of Computing Science, Simon Fraser University
  • , Russell GreinerAffiliated withDepartment of Computing Science, University of Alberta
  • , Hassan KhosraviAffiliated withSchool of Computing Science, Simon Fraser University

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This paper presents the I-map hybrid algorithm for selecting, given a data sample, a linear Gaussian model whose structure is a directed graph. The algorithm performs a local search for a model that meets the following criteria: (1) The Markov blankets in the model should be consistent with dependency information from statistical tests. (2) Minimize the number of edges subject to the first constraint. (3) Maximize a given score function subject to the first two constraints. Our local search is based on Graph Equivalence Search (GES); we also apply the recently developed SIN statistical testing strategy to help avoid local minima. Simulation studies with GES search and the BIC score provide evidence that for nets with 10 or more variables, the hybrid method selects simpler graphs whose structure is closer to the target graph.