Paper
- F. J. Díez. Local
conditioning in Bayesian
networks. Artificial
Intelligence, 87 (1996)
1-20.
- 19 pages. PostScript
(234 KB), zip
version (68 KB), BibTeX
entry.
Abstract
Local conditioning is an exact algorithm for computing probability in
Bayesian networks, developed as an extension of Kim and Pearl's
algorithm for singly-connected networks. A list of variables
associated to each node guarantees that only the nodes inside a loop
are conditioned on the variable which breaks it. The main advantage of
this algorithm is that it computes the probability directly on the
original network instead of building a cluster tree, and this can save
time when debugging a model and when the sparsity of evidence allows a
pruning of the network. The algorithm is also advantageous when some
families in the network interact through AND/OR gates. A parallel
implementation of the algorithm with a processor for each node is
possible even in the case of multiply-connected networks.
Javier Díez
/ Last update: February 22 1999.