| 期刊全稱 | Bayesian Networks and Decision Graphs | | 影響因子2023 | Finn V. Jensen,Thomas D. Nielsen | | 視頻video | http://file.papertrans.cn/182/181864/181864.mp4 | | 發(fā)行地址 | Gives a well-founded practical introduction to Bayesian networks.Includes presentation of the most efficient algorithm for solving influence diagrams.Includes supplementary material: | | 學(xué)科分類 | Information Science and Statistics | | 圖書封面 |  | | 影響因子 | .Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis...The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. ..The book is a new edition of .Bayesian Networks and Decision Graphs. by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian ne | | Pindex | Textbook 2007Latest edition |
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