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Consistency in stochastic networks

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Veröffentlichung
2012-09-07
DOI
10.18725/OPARU-2447
Bericht


Authors
Hasseln, Hermann von
Martignon, Laura
Faculties
Fakultät für Ingenieurwissenschaften und Informatik
Ulm serial
Ulmer Informatik-Berichte
License
Standard
https://oparu.uni-ulm.de/xmlui/license_v3
Abstract
Stochastic networks are given by graphs, whose vertices (nodes, neurons or spins) can take one out of a finite number of states at any given time. The way each vertex changes its state over time is determined by the edges connecting it with other vertices. The edges represent probabilistic dependencies. Updating is usually performed in a parallel or sequential way. Stochastic networks are used to model expert sytems; in this context confidence numbers are given as dependencies between vertices and the problem is to see, to what extent they are stochastically consistent. Given a graph and a set (or subset) of confidence numbers, we give a procedure that, starting from these confidence numbers, leads to local characteristics which are consistent with the graph.
Date created
1992
Subject Headings
Stochastisches Modell [GND]
Expert systems (Computer science) [LCSH]
Markov processes [LCSH]
Stochastic models [LCSH]
Dewey Decimal Group
DDC 004 / Data processing & computer science

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Hasseln, Hermann von; Martignon, Laura (2012): Consistency in stochastic networks. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-2447

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