Titlebook: Hybrid Random Fields; A Scalable Approach Antonino Freno,Edmondo Trentin Book 2011 Springer Berlin Heidelberg 2011 Bayesian Networks.Data

Champion

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Bayesian Networks,le ., possibly taking any one of the three different values . (win), . (lose), or . (draw), according to a certain (yet, unknown) probability distribution. As unknown as this distribution may be, some probabilistic reasoning, and your prior knowledge of the situation, may help you out predicting the

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1868-4394 rkov random fields should not miss it..-- Marco Gori, Università degli Studi di Siena.Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they o978-3-642-26818-2978-3-642-20308-4Series ISSN 1868-4394 Series E-ISSN 1868-4408

Processes

Introduction,aphical models is to offer “a mechanism for exploiting structure in complex distributions to describe them compactly, and in a way that allows them to be constructed and utilized effectively” (Daphne Koller and Nir Friedman, 2009 [174]). They “have their origin in several scientific areas”, and “the

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puzzle

Markov Random Fields,e ranking of your team in the domestic soccer league championship at any given time . throughout the current season. In this setup, it is reasonable to assume that . is a discrete time index, denoting .-th game in the season and ranging from . = 1 (first match of the tournament) to . = . (season fin

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ingestion

Extending Hybrid Random Fields: Continuous-Valued Variables,curring in the real world, it is necessary to describe them in a proper feature space, such that each phenomenon can be thought of as a random variable (if a single attribute is used), or a random vector (if multiple features are extracted). The feature extraction process is crucial for the success