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Titlebook: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis; Uffe B. Kj?rulff,Anders L. Madsen Book 2013Latest edition

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期刊全稱Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
影響因子2023Uffe B. Kj?rulff,Anders L. Madsen
視頻videohttp://file.papertrans.cn/182/181866/181866.mp4
發(fā)行地址Comprehensive introduction to understand, construct, and analyze probabilistic networks.Second Edition features six new sections covering such topics as structure learning and parameter tuning.New app
學(xué)科分類Information Science and Statistics
圖書封面Titlebook: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis;  Uffe B. Kj?rulff,Anders L. Madsen Book 2013Latest edition
影響因子.Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition,.?provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix.? 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 or 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 developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. .
Pindex Book 2013Latest edition
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Networks of a graph and the interactions (direct dependences) as directed edges (links or arcs) between the vertices. Any pair of unconnected vertices of such a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be re
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Probabilitiesrsive factorization of a joint probability distribution into a product of lower-dimensional conditional probability distributions. First, any joint probability distribution can be decomposed (or factorized) into a product of conditional distributions of different dimensionality, where the dimensiona
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Eliciting the Model]. The structure of a probabilistic network is often referred to as the qualitative part of the network, whereas the parameters are often referred to as its quantitative part. As the parameters of a model are determined by its structure, the model elicitation process always proceeds in two consecuti
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Data-Driven Modelingases and expert knowledge consists of two main steps. The first step is to induce the structure of the model, that is, the DAG, while the second step is to estimate the parameters of the model as defined by the structure. In this chapter we consider only discrete Bayesian networks. Thus, the task of
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