The IMAP Hybrid Method for Learning Gaussian Bayes Nets

  • Oliver Schulte
  • Gustavo Frigo
  • Russell Greiner
  • Hassan Khosravi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6085)


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.


Local Search Bayesian Network Directed Acyclic Graph Independence Test False Acceptance Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oliver Schulte
    • 1
  • Gustavo Frigo
    • 1
  • Russell Greiner
    • 2
  • Hassan Khosravi
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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