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On modeling of if‐then rules for probabilistic inference
Author(s) -
Nguyen Hung T.,
Goodman I. R.
Publication year - 1994
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.4550090406
Subject(s) - probabilistic logic , computer science , conditional probability , inference , bayesian network , probabilistic logic network , artificial intelligence , probabilistic relevance model , rule of inference , bayesian inference , boolean function , expert system , machine learning , theoretical computer science , bayesian probability , data mining , algorithm , mathematics , probabilistic analysis of algorithms , statistics , autoepistemic logic , multimodal logic , description logic
We identify various situations in probabilistic intelligent systems in which conditionals (rules) as mathematical entities as well as their conditional logic operations are needed. In discussing Bayesian updating procedure and belief function construction, we provide a new method for modeling if… then rules as Boolean elements, and yet, compatible with conditional probability quantifications. © 1994 John Wiley & Sons, Inc.