Consistency in stochastic networks
Bericht
Autoren
Hasseln, Hermann von
Martignon, Laura
Fakultäten
Fakultät für Ingenieurwissenschaften und InformatikUlmer Schriftenreihe
Ulmer Informatik-Berichte
Zusammenfassung
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.
Erstellung / Fertigstellung
1992
Normierte Schlagwörter
Stochastisches Modell [GND]Expert systems (Computer science) [LCSH]
Markov processes [LCSH]
Stochastic models [LCSH]
DDC-Sachgruppe
DDC 004 / Data processing & computer scienceMetadata
Zur LanganzeigeZitiervorlage
Hasseln, Hermann von; Martignon, Laura (2012): Consistency in stochastic networks. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-2447