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Simulation metamodeling with dynamic Bayesian networks
European Journal of Operational Research, 2011This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions.
Virtanen, Kai, Poropudas, Jirka
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2010
Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled.
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Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled.
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Topological Dynamic Bayesian Networks
2010 20th International Conference on Pattern Recognition, 2010The objective of this research is to embed topology within the dynamic Bayesian network (DBN) formalism. This extension of a DBN (that encodes statistical or causal relationships) to a topological DBN (TDBN) allows continuous mappings (e.g., topological homeomorphisms), topological relations (e.g., homotopy equivalences) and invariance properties (e.g.,
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Bayesian compression for dynamically expandable networks
Pattern Recognition, 2022Abstract This paper develops Bayesian Compression for Dynamically Expandable Network (BCDEN), which can learn a compact model structure with preserving the accuracy in a continual learning scenarios. Dynamically Expandable Network (DEN) is efficiently trained by performing selective retraining, dynamically expands network capacity with only the ...
Yang Yang, Bo Chen, Hongwei Liu
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Pedestrian dynamics via Bayesian networks
AIP Conference Proceedings, 2014Studies on pedestrian dynamics have vital applications in crowd control management relevant to organizing safer large scale gatherings including pilgrimages. Reasoning pedestrian motion via computational intelligence techniques could be posed as a potential research problem within the realms of Artificial Intelligence.
Ibrahim Venkat +2 more
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Dynamic Bayesian Network Library
2009Anwendungen, wie sie beispielsweise bei autonomen, mobilen Systemen vorkommen, erfordern die Bearbeitung und Auswertung von heterogenen, unsicheren Messwerten. Probabilistische Ansatze bieten die Moglichkeit, derartige Probleme zu losen. Prasentiert wird die DBNL, eine C++ Bibliothek, welche die Reprasentation und Inferenz von dynamischen, hybriden ...
Ralf Kohlhaas +3 more
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Dynamic Bayesian Networks for Prognosis
Annual Conference of the PHM Society, 2013In this paper, a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN) is proposed. Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats from various sources and give a probabilistic representation of a ...
Gregory Bartram, Sankaran Mahadevan
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Measuring network security using dynamic bayesian network
Proceedings of the 4th ACM workshop on Quality of protection, 2008Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics have generally focused on measuring individual vulnerabilities without considering their combined effects.
Marcel Frigault +3 more
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Dynamic Bayesian networks for visual recognition of dynamic gestures
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2002Summary: Dynamic Bayesian networks are a powerful representation to describe processes that vary over time inside a stochastic framework. This paper describes an online visual recognition system to recognize a set of five dynamic gestures executed with the user's right hand using dynamic Bayesian networks for recognition.
Avilés-Arriaga, Héctor Hugo +1 more
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Dynamic Bayesian Networks for Student Modeling
IEEE Transactions on Learning Technologies, 2017Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling.
Tanja Kaser +3 more
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