Results 31 to 40 of about 5,728,670 (281)
Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective
Doğal Afetler ve Çevre Dergisi, 2018 Natural hazard assessments are core to risk definition and early warning systems and play a fundamental role in the prevention of major damages. Traditional hazard identification methods are static.
Ipek Yilmaz, Derya Ozturk
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Bayesian Learning of Dynamic Multilayer Networks
J. Mach. Learn. Res., 2016 A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges.
DURANTE, DANIELE +2 more
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Algorithms, 2020 Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models.
Austin D. Lewis, Katrina M. Groth
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Non-homogeneous dynamic Bayesian networks for continuous data [PDF]
, 2011 : Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed.
Husmeier, D. +5 more
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Searching multiregression dynamic models of resting-state fMRI networks using integer programming [PDF]
, 2015 A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in a graphical way. One of the advantages of this class is that, in contrast to many other Dynamic Bayesian Networks, the ...
Smith, Jim +9 more
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Filtering in hybrid dynamic Bayesian networks [PDF]
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004 We demonstrate experimentally that inference in a complex hybrid dynamic Bayesian network (DBN) is possible using the 2-time slice DBN (2T-DBN) from (D. Koller et al., Sequential Monte Carlo Methods in Practice: p.445-464, Springer-Verlag, NY, 2000) to model fault detection in a watertank system.
Andersen, Morten Nonboe +2 more
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Quantifying resilience of socio-ecological systems through dynamic Bayesian networks
Frontiers in Forests and Global Change, 2022 Quantifying resilience of socio-ecological systems (SES) can be invaluable to delineate management strategies of natural resources and aid the resolution of socio-environmental conflicts.
Felipe Franco-Gaviria +4 more
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Towards data-centric control of sensor networks through Bayesian dynamic linear modelling [PDF]
, 2015 Wireless sensor networks usually operate in dynamic, stochastic environments. While the behaviour of individual nodes is important, they are better seen as contributors to a larger mission, and managing the sensing quality and performance of these ...
Dobson, Simon Andrew +3 more
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Cerebral modeling and dynamic Bayesian networks [PDF]
Artificial Intelligence in Medicine, 2004 The understanding and the prediction of the clinical outcomes of focal or degenerative cerebral lesions, as well as the assessment of rehabilitation procedures, necessitate knowing the cerebral substratum of cognitive or sensorimotor functions. This is achieved by activation studies, where subjects are asked to perform a specific task while data of ...
Vincent Labatut +4 more
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IEEE Access, 2018 A novel reliability evaluation methodology of complex systems is proposed using dynamic object-oriented Bayesian networks (DOOBNs). This modeling methodology consists of two main phases, namely, construction phases for object-oriented Bayesian networks ...
Xiaobing Yuan +6 more
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