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Reasoning with incomplete information in a multivalued multiway causal tree using the maximum entropy formalism
Author(s) -
Holmes Dawn E.,
Rhodes Paul C.
Publication year - 1998
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/(sici)1098-111x(199809)13:9<841::aid-int4>3.0.co;2-i
Subject(s) - formalism (music) , probabilistic logic , causal model , computer science , principle of maximum entropy , entropy (arrow of time) , artificial intelligence , causal structure , bayesian network , mathematics , theoretical computer science , statistics , art , musical , physics , quantum mechanics , visual arts
Expert systems that use causal probabilistic networks require the user to supply complete causal information regarding the causal probabilities to be used. This paper describes a method using the maximum entropy formalism that enables such expert systems to operate with incomplete causal information for certain classes of causal networks. It has been shown that, in the general case, solving causal networks using maximum entropy techniques is NP‐complete. However, we show that for multivalued causal multiway trees—a nontrivial class of causal networks—the problem of estimating missing information is only linear. © 1998 John Wiley & Sons, Inc.