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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1613-9011 ing. ..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 develo978-1-4419-2546-6978-0-387-74101-7Series ISSN 1613-9011 Series E-ISSN 2197-4128

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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

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Michael St.Pierre DEAA,Gesine Hofingerbe 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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Michael St.Pierre DEAA,Gesine Hofingerto 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

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Managing Errors During Trainingan approximation of a problem domain that is designed to be applied according to the assumptions as determined by the background condition or context of the model. If a model is used under circumstances not consistent with the background condition, the results will in general be unreliable. The evid

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Johannes Bauer,Christian Harteisterior probability distribution over a hypothesis variable given a set of evidence. Similarly, the solution to a decision making problem is an optimal decision given a set of evidence. When faced with a reasoning or decision making problem, we may have the option to consult additional information so