Title
An empirical study of Bayesian network inference with simple propagation.
Abstract
We propose Simple Propagation (SP) as a new join tree propagation algorithm for exact inference in discrete Bayesian networks. We establish the correctness of SP. The striking feature of SP is that its message construction exploits the factorization of potentials at a sending node, but without the overhead of building and examining graphs as done in Lazy Propagation (LP). Experimental results on optimal (or close to optimal) join trees built from numerous benchmark Bayesian networks show that SP is often faster than LP.
Year
DOI
Venue
2018
10.1016/j.ijar.2017.10.005
International Journal of Approximate Reasoning
Keywords
Field
DocType
Bayesian networks,Exact inference,Join tree propagation
Correctness,Theoretical computer science,Artificial intelligence,Empirical research,Belief propagation,Discrete mathematics,Graph,Inference,Exploit,Bayesian network,Factorization,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
92
1
0888-613X
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Cory J. Butz138340.80
Jhonatan de S. Oliveira267.43
André E. dos Santos357.02
Anders L. Madsen438440.41