Results 231 to 240 of about 42,084 (260)

Dynamic Bayesian Networks for Prognosis

Annual Conference of the PHM Society, 2013
In 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
openaire   +1 more source

Dynamic Bayesian Networks: A Factored Model of Probabilistic Dynamics

2012
The modeling and analysis of probabilistic dynamical systems is becoming a central topic in the formal methods community. Usually, Markov chains of various kinds serve as the core mathematical formalism in these studies. However, in many of these settings, the probabilistic graphical model called dynamic Bayesian networks (DBNs) [4] can be amore ...
Sucheendra K. Palaniappan, P. S. Thiagarajan   +1 more
openaire   +1 more source

Dynamic Bayesian Network Library

2009
Anwendungen, 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, Ferdinand Szekeresch, Tobias Gindele, Rüdiger Dillmann   +3 more
openaire   +1 more source

Dynamic Bayesian Networks for Fault Prognosis

Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 2023
Ojas Pradhan, Jin Wen, Mengyuan Chu, Zheng O'Neill   +3 more
openaire   +1 more source

Traffic congestion propagation inference using dynamic Bayesian graph convolution network

Transportation Research Part C: Emerging Technologies, 2022
Sen Luan, Ruimin Ke, Zhou Huang
exaly  

A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect

International Journal of Production Research, 2021
Zhongzheng Liu, Feng Chu, Feifeng Zheng   +2 more
exaly  

Dynamic and Temporal Bayesian Networks

2015
Dynamic Bayesian network models extend BNs to represent the temporal evolution of a certain process. There are two basic types of Bayesian network models for dynamic processes: state based and event based. Dynamic Bayesian networks are state-based models that represent the state of each variable at discrete time intervals.
openaire   +1 more source

Multi-dynamic Bayesian Networks

2007
Karim Filali, Jeff A. Bilmes
openaire   +2 more sources

Bayesian network for dynamic variable structure learning and transfer modeling of probabilistic soft sensor

Journal of Process Control, 2021
Lingquan Zeng, Zhiqiang Ge
exaly  

Maritime accident risk estimation for sea lanes based on a dynamic Bayesian network

Maritime Policy and Management, 2020
Meizhi Jiang, Jing Lu
exaly