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IJAR
2008

Approximate algorithms for credal networks with binary variables

11 years 8 months ago
Approximate algorithms for credal networks with binary variables
This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the IPE and SV2U algorithms are respectively based on Localized Partial Evaluation and variational techniques. Key words: Credal networks, loopy belief propagation, variational methods, 2U algorithm
Jaime Shinsuke Ide, Fabio Gagliardi Cozman
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where IJAR
Authors Jaime Shinsuke Ide, Fabio Gagliardi Cozman
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