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Titlebook: Probabilistic Logics and Probabilistic Networks; Rolf Haenni,Jan-Willem Romeijn,Jon Williamson Book 2011 Springer Science+Business Media B

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書目名稱Probabilistic Logics and Probabilistic Networks
編輯Rolf Haenni,Jan-Willem Romeijn,Jon Williamson
視頻videohttp://file.papertrans.cn/757/756803/756803.mp4
概述Presents a groundbreaking framework within which various approaches to probabilistic logic naturally fit.Shows that there is potential to develop a general computational method for computing the requi
叢書名稱Synthese Library
圖書封面Titlebook: Probabilistic Logics and Probabilistic Networks;  Rolf Haenni,Jan-Willem Romeijn,Jon Williamson Book 2011 Springer Science+Business Media B
描述While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework.The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.
出版日期Book 2011
關鍵詞Bayesian networks; Bayesian statistical inference; Computational logic; Computational methods; Credal ne
版次1
doihttps://doi.org/10.1007/978-94-007-0008-6
isbn_softcover978-94-007-3443-2
isbn_ebook978-94-007-0008-6Series ISSN 0166-6991 Series E-ISSN 2542-8292
issn_series 0166-6991
copyrightSpringer Science+Business Media B.V. 2011
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Rolf Haenni,Jan-Willem Romeijn,Jon WilliamsonPresents a groundbreaking framework within which various approaches to probabilistic logic naturally fit.Shows that there is potential to develop a general computational method for computing the requi
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978-94-007-3443-2Springer Science+Business Media B.V. 2011
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Probabilistic Argumentationheory is driven by the general idea of putting forward the pros and cons of a proposition or hypothesis in question. The weights of the resulting logical arguments and counter-arguments are measured by ., which are then turned into (sub-additive.) degrees of . and (super-additive) degrees of ..
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Credal and Bayesian Networkssm, often a single probability function. This restriction will be important in what follows, since it will allow us to exploit the computational machinery of probabilistic networks—in particular . and .—to help us answer the fundamental question.
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