Sensitivity Analysis Probabilistic Graphical by Chan Hei (16 results)
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Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2009
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Taschenbuch. Condition: Neu. Sensitivity Analysis of Probabilistic Graphical Models | Theoretical Results and Their Applications on Bayesian Network Modeling and Inference | Hei Chan | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639136951 | Verantwortliche Person für die EU: VDM Verlag Dr. Müller, Brivibas Gatve 197…, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chan HeiHei Chan is a research fellow at the Center for Service Research nof the National Institute of Advanced Industrial Science and nTechnology (AIST), in Tokyo, Japan. He re…ceived his Ph.D. degree nin computer science at the Univ.
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Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2009
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Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2009
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Probabilistic graphical models such as Bayesian networks are widely used for large-scale data analysis in various fields such as customer data analysis and medical diagnosis, as they model probabilistic knowledge naturally and allow the…use of efficient inference algorithms to draw conclusions from the model. Sensitivity analysis of probabilistic graphical models is the analysis of the relationships between the inputs (local beliefs), such as network parameters, and the outputs (global beliefs), such as values of probabilistic queries, and addresses the central research problem of how beliefs will be changed when we incorporate new information to the current model. This book provides many theoretical results, such as the assessment of global belief changes due to local belief changes, the identification of local belief changes that induce certain global belief changes, and the quantifying of belief changes in general. These results can be applied on the modeling and inference of Bayesian networks, and provide a critical tool for the researchers, developers, and users of Bayesian networks during the process of probabilistic data modeling and reasoning.
