Improvement of Decisions about Project Risk Management Based on Bayesian Network
The project risk management is a hot topic in the field of current management. As to the problems of lacking data to construct the decision model and lacking quantitative ways to inspect model,this paper sets about from the Bayesian network and puts forwa
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Improvement of Decisions about Project Risk Management Based on Bayesian Network Jinpeng He and Hongchang Mei
Abstract The project risk management is a hot topic in the field of current management. As to the problems of lacking data to construct the decision model and lacking quantitative ways to inspect model,this paper sets about from the Bayesian network and puts forward a method of constructing Bayesian belief network with chain inference method, diagnosis inference method and the guidance of experts under the condition of lacking data. The network are not only used to display project risk directly in the form of graph but also used to calculate local risk and overall risk. In addition, the paper has discussed the way of using Bayesian belief construction algorithm to check out the similarity of the network structure and sample in a quantitative way combined with B-Course tool. In order to save a large amount of time of testing, the paper also optimizes the traditional mathematical Bayesian algorithm with the method of compression. Thus the decision maker can improve the utilized efficiency of time. Finally, this paper has summarized the advantages and disadvantages of this new method and provides a different way of thinking on decision-making of risk management. Keywords Project risk algorithm
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Bayesian network
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Quantitative test
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Compressive
100.1 Background of Researching The founding work of Bayesian school is based on Bayes’s paper “comments on the solution of several probability problem” [12]. This thesis had not been published during his lifetime because he felt that his academic research had some drawbacks. J. He (B) · H. Mei Management School of Chongqing, Technology and Business University, Chongqing 400067, People’s Republic of China e-mail: [email protected]
J. Xu et al. (eds.), Proceedings of the Eighth International Conference on Management Science and Engineering Management, Advances in Intelligent Systems and Computing 281, DOI: 10.1007/978-3-642-55122-2_100, © Springer-Verlag Berlin Heidelberg 2014
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But after his death, it is published by his friend. The famous mathematician Laplace had derived an important rule called “succession rule” in 1812 using Bayesian method.So Bayesian method and the theory were gradually understood and valued at the 20th century. The Italian Firat and the British Geoffroy had made important contribution to the theory of Bayesian School [14]. After the second world war, Wardle had proposed statistical decision theory and Bayes theorem occupies an important position in this theory [4]. In the 1950s, represented by Robbins, some people had put forward the combination of empirical Bayes method and the classic methods. The combination had aroused wide attention from all walks of life. It showed its advantages soon and became an active way in some directions [12]; In the late 80s, Bayesian networks is successfully applied to the expression of expert system about the knowledge of uncertainty system and its technology of reasoning is
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