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Bayesian Network Modeling Techniques And The Application In Decision-Making

Posted on:2007-03-29Degree:DoctorType:Dissertation
Country:ChinaCandidate:X X HuFull Text:PDF
GTID:1119360212458385Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Decision problems are always uncertain due to their ambiguity and randomicity, imperfection and nonproficiency of decision information, finity of human's cognitive abilities and otherness of subjective cognizance from objective realities. The existence of uncertainty raises the difficulty in making decisions. Therefore, decision theories and methods in uncertain circumstances is the frontier area in decision-science.In recent years, many of resolvents to uncertain problems have appeared such as evidence theory, Bayesian Network, fuzzy set and rough set with the development of mathematical theory and artificial intelligence. Among these methods, Bayesian Network has a good many advantages such as intuitionistic expression abilities and strong knowledge reasoning especially at uncertain reasoning which make it a hot topic in uncertain theory.Aiming at Bayesian Network's shortage of low study efficiency and difficult to be constructed, this dissertation mainly discusses Bayesian Network modeling techniques and its application in decision-making, including:(1) The dissertation summarizes the categories of uncertain problems, the development of Bayesian Network and research has been made on it, its prospect of being applied in management decision and intelligent decision support system. It also expatiates on Bayesian Network's structure and characteristics, reasoning methods and all kinds of expanding models of Bayesian Networks.(2) The structural learning methods of Bayesian Network are discussed. A constructing method of Bayesian Network combining knowledge with data is presented. Firstly, the experts assign belief of networks structure, which is combined by evidence theory. The structures with highest belief are considered as correct ones. Secondly, we select the best one from those structures given by the experts using a learning arithmetic. Eliminating insignificant structures of network by expert knowledge, this method can avoid mass blindly hunting and speed up study rate.(3) The knowledge-based modeling method of Bayesian Network is discussed. A modeling method based on case and rule reasoning is presented. By saving historical Bayesian models to case-base and designing two indexes: degree of similarity and degree of irrelevance, we can match models with case-based reasoning when encounter with a new problem, and get a same or similar case. If the cased-based reasoning proved to be resultless , the system will continue modeling procedure by rule-based reasoning .This method reuses Bayesian Network as a whole, thus to improve the modeling efficiency .(4) The Bayesian Network modeling principles are summarized, and the modeling course for...
Keywords/Search Tags:Uncertain problems, Bayesian Networks, Intelligent Decision Support System, Modeling, Case-based Reasoning, Supply Chain Management
PDF Full Text Request
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