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Titlebook: Advances in Bayesian Networks; José A. Gámez,Serafín Moral,Antonio Salmerón Book 2004 Springer-Verlag Berlin Heidelberg 2004 Bayesian netw

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期刊全称Advances in Bayesian Networks
影响因子2023José A. Gámez,Serafín Moral,Antonio Salmerón
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发行地址Includes supplementary material:
学科分类Studies in Fuzziness and Soft Computing
图书封面Titlebook: Advances in Bayesian Networks;  José A. Gámez,Serafín Moral,Antonio Salmerón Book 2004 Springer-Verlag Berlin Heidelberg 2004 Bayesian netw
影响因子.In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, .Advances in Bayesian Networks. presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval..
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Book 2004ce. In addition, .Advances in Bayesian Networks. presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval..
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https://doi.org/10.1007/978-1-349-20672-8al independence in the Bayesian network. We discuss propagation tasks where conditional independence is not ignored in the construction of the hierarchical junction tree and in the propagation tasks. We also discuss their use for efficient exact forecasting in dynamic Bayesian network with heterogeneous evolution.
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War-time Control and Nationalization, the decision problem elements and b) can be evaluated providing optimal policies for decision makers. These reasons explain the extensive body of work devoted to IDs and the use of this tool in real-world applications.
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1434-9922 erienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a
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https://doi.org/10.1007/978-3-319-77863-1cooperatively the d-sepset condition through message passing. Each message reveals only partial information on the adjacency of a shared node in an agent’s local network. Hence, the method respects agent’s privacy, protects agent vendors’ know-how, and promotes integration of multiagent systems from independently developed agents.
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