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Foundations of compositional models: inference

International Journal of General Systems, 2021
Compositional models, as an alternative to Bayesian networks, are assembled from a system of low-dimensional distributions. Thus the respective apparatus falls fully into probability theory.
Vladislav Bína   +2 more
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Web Services: Foundation and Composition

Electronic Markets, 2003
Today both business analysts and information systems engineers attribute a great potential to Web services as a vehicle to simplify the interoperability of services offered by different organizations in electronic business scenarios. In this paper, the Service Oriented Architecture is explained as the foundation of this new technology.
Jens Hündling, Mathias Weske
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Foundations for Circular Compositional Reasoning

2001
Compositional proofs about systems of many components require circular reasoning principles in which properties of other components need to be assumed in proving the properties of each individual component. A number of such circular assume-guarantee rules have been proposed for different concurrency models and different forms of property specifications.
Mahesh Viswanathan 0001   +1 more
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Dagstuhl Seminar on the Foundations of Composite Event Recognition

ACM SIGMOD Record, 2021
Composite event recognition (CER) is concerned with continuously matching patterns in streams of 'event' data over (geographically) distributed sources. This paper reports the results of the Dagstuhl Seminar "Foundations of Composite Event Recognition" held in 2020.
Artikis, A   +3 more
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Foundations of compositional models: structural properties

International Journal of General Systems, 2014
The paper is a follow-up of [R.J.: Foundations of compositional model theory. IJGS, 40(2011): 623–678], where basic properties of compositional models, as one of the approaches to multidimensional probability distributions representation and processing, were introduced.
Radim Jirousek, Václav Kratochvíl
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Foundations of compositional model theory

International Journal of General Systems, 2011
Graphical Markov models, most of all Bayesian networks, have become a very popular way for multidimensional probability distribution representation and processing. What makes representation of a very-high-dimensional probability distribution possible is its independence structure, i.e.
openaire   +1 more source

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