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Titlebook: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis; Uffe B. Kjærulff,Anders L. Madsen Book 20081st edition Spr

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发表于 2025-3-21 17:48:49 | 显示全部楼层 |阅读模式
期刊全称Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
影响因子2023Uffe B. Kjærulff,Anders L. Madsen
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发行地址Comprehensive introduction to probabilistic networks.Written specifically for practitioners of applied artificial intelligence.Complete guide to understand, construct, and analyze probabilistic networ
学科分类Information Science and Statistics
图书封面Titlebook: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis;  Uffe B. Kjærulff,Anders L. Madsen Book 20081st edition Spr
影响因子.Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. ..Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. ..The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been develo
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发表于 2025-3-21 21:04:16 | 显示全部楼层
Michael St.Pierre DEAA,Gesine Hofingera process of deriving conclusions (new pieces of knowledge) by manipulating a (large) body of knowledge, typically including definitions of entities (objects, concepts, events, phenomena, etc.), relations among them, and observations of states (values) of some of the entities.
发表于 2025-3-22 03:06:14 | 显示全部楼层
Menschliche Wahrnehmung: Die Sicht der Dinge a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.
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Networks a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.
发表于 2025-3-22 13:41:22 | 显示全部楼层
Conflict Analysisence need not be inconsistent with the model in order for the results to be unreliable. It may be that evidence is simply in conflict with the model. This implies that the model in relation to the evidence may be weak and therefore the results may be unreliable.
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发表于 2025-3-22 21:30:12 | 显示全部楼层
Managing Errors During Trainingence need not be inconsistent with the model in order for the results to be unreliable. It may be that evidence is simply in conflict with the model. This implies that the model in relation to the evidence may be weak and therefore the results may be unreliable.
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发表于 2025-3-23 05:43:35 | 显示全部楼层
Book 20081st editionlied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. ..Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive
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