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Matilda: A visual tool for modeling with Bayesian networks
Author(s) -
Boneh T.,
Nicholson A. E.,
Sonenberg E. A.
Publication year - 2006
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20175
Subject(s) - bayesian network , computer science , domain (mathematical analysis) , graphical model , data mining , bayesian probability , machine learning , artificial intelligence , mathematics , mathematical analysis
Abstract A Bayesian Network (BN) consists of a qualitative part representing the structural assumptions of the domain and a quantitative part, the parameters. To date, knowledge engineering support has focused on parameter elicitation, with little support for designing the graphical structure. Poor design choices in BN construction can impact the network's performance, network maintenance, and the explanatory power of the output. We present a tool to help domain experts examine BN structure independently of the parameters. Our qualitative evaluation of the tool shows that it can help in identifying possible structural modeling errors and, hence, improve the quality of BN models. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1127–1150, 2006.