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

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31#
發(fā)表于 2025-3-27 00:03:25 | 只看該作者
Probabilitiesiven by a graphical structure in the form of an acyclic, directed graph (DAG) that represents the (conditional) dependence and independence properties of a joint probability distribution defined over a set of variables that are indexed by the vertices of the DAG.
32#
發(fā)表于 2025-3-27 05:06:56 | 只看該作者
Solving Probabilistic Networksto support our reasoning about events and decisions in a domain with inherent uncertainty. The fundamental idea of solving a probabilistic network is to exploit the structure of the knowledge base to reason efficiently about the events and decisions of the domain taking the inherent uncertainty into account.
33#
發(fā)表于 2025-3-27 08:48:59 | 只看該作者
Sensitivity Analysisncertainty the posterior probability of a single hypothesis variable is sometimes of interest. When the evidence set consists of a large number of findings or even when it consists of only a small number of findings questions concerning the impact of subsets of the evidence on the hypothesis or a competing hypothesis emerge.
34#
發(fā)表于 2025-3-27 10:39:55 | 只看該作者
35#
發(fā)表于 2025-3-27 16:03:51 | 只看該作者
978-1-4419-2546-6Springer-Verlag New York 2008
36#
發(fā)表于 2025-3-27 19:53:12 | 只看該作者
37#
發(fā)表于 2025-3-27 22:28:13 | 只看該作者
38#
發(fā)表于 2025-3-28 03:25:33 | 只看該作者
39#
發(fā)表于 2025-3-28 08:46:13 | 只看該作者
40#
發(fā)表于 2025-3-28 10:30:27 | 只看該作者
Introductionbe programmed to execute an arbitrary set of manipulations on numbers and symbols. Solving an intellectually challenging task can be characterized as a process of deriving conclusions (new pieces of knowledge) by manipulating a (large) body of knowledge, typically including definitions of entities (
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