Uncertain Evidence in Bayesian Networks: Presentation and Comparison on a Simple Example

Communications in Computer and Information Science - Advances in Computational Intelligence ◽

10.1007/978-3-642-31718-7_5 ◽

2012 ◽

pp. 39-48 ◽

Cited By ~ 4

Author(s):

Ali Ben Mrad ◽

Veronique Delcroix ◽

Mohamed Amine Maalej ◽

Sylvain Piechowiak ◽

Mohamed Abid

Keyword(s):

Bayesian Networks ◽

Uncertain Evidence

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Belief Update in Bayesian Networks Using Uncertain Evidence

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) ◽

10.1109/ictai.2006.39 ◽

2006 ◽

Cited By ~ 13

Author(s):

Rong Pan ◽

Yun Peng ◽

Zhongli Ding

Keyword(s):

Bayesian Networks ◽

Uncertain Evidence

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BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCES

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488510006696 ◽

2010 ◽

Vol 18 (05) ◽

pp. 539-564 ◽

Cited By ~ 31

Author(s):

YUN PENG ◽

SHENYONG ZHANG ◽

RONG PAN

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Efficient Algorithms ◽

Iterative Proportional Fitting ◽

Jeffrey’S Rule ◽

Fitting Procedure ◽

Uncertain Evidence ◽

Hard Evidence

This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain evidence. Two types of uncertain evidences are identified: virtual evidence (reflecting the uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an event one observes). Each of the two types of evidence has its own characteristics and obeys a belief update rule that is different from hard evidence, and different from each other. The particular emphasis is on belief update with multiple uncertain evidences. Efficient algorithms for BN reasoning with consistent and inconsistent uncertain evidences are developed, and their convergences analyzed. These algorithms can be seen as combining the techniques of traditional BN reasoning, Pearl's virtual evidence method, Jeffrey's rule, and the iterative proportional fitting procedure.

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