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The IMAP Hybrid Method for Learning Gaussian Bayes Nets

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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.


O. Schulte, G. Frigo, H. Khosravi, R. Greiner. "The IMAP Hybrid Method for Learning Gaussian Bayes Nets". Canadian Conference on Artificial Intelligence (CAI), April 2010.

Keywords: machine learning, Bayesian belief nets, Gaussian
Category: In Conference


  author = {Oliver Schulte and Gustavo Frigo and Hassan Khosravi and Russ
  title = {The IMAP Hybrid Method for Learning Gaussian Bayes Nets},
  booktitle = {Canadian Conference on Artificial Intelligence (CAI)},
  year = 2010,

Last Updated: July 19, 2010
Submitted by Nelson Loyola

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