The IMAP Hybrid Method for Learning Gaussian Bayes Nets
Full Text:
imap-llncs.pdf 
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.
Citation
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 |
BibTeX
@incollection{Schulte+al:CAI10,
author = {Oliver Schulte and Gustavo Frigo and Hassan Khosravi and Russ
Greiner},
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 
