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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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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.
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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.
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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.
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978-1-4419-2546-6Springer-Verlag New York 2008
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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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