Results 21 to 30 of about 7,371,142 (245)
Bayesian Spillover Graphs for Dynamic Networks
CoRR, 2022 We present Bayesian Spillover Graphs (BSG), a novel method for learning temporal relationships, identifying critical nodes, and quantifying uncertainty for multi-horizon spillover effects in a dynamic system. BSG leverages both an interpretable framework via forecast error variance decompositions (FEVD) and comprehensive uncertainty quantification via ...
Grace Deng, David S. Matteson
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Dynamic Bayesian networks for meeting structuring [PDF]
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004 The paper is about the automatic structuring of multiparty meetings using audio information. We have used a corpus of 53 meetings, recorded using a microphone array and lapel microphones for each participant. The task was to segment meetings into a sequence of meeting actions, or phases.
Alfred Dielmann, Steve Renals
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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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Opinion Dynamics with Bayesian Learning
Complexity, 2020 Bayesian learning is a rational and effective strategy in the opinion dynamic process. In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network.
Aili Fang +3 more
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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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Machines, 2021 The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies.
Valentina Zaccaria +2 more
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BMC Bioinformatics, 2023 A nonhomogeneous dynamic Bayesian network model, which combines the dynamic Bayesian network and the multi-change point process, solves the limitations of the dynamic Bayesian network in modeling non-stationary gene expression data to a certain extent ...
Jiayao Zhang +2 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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Applied Sciences, 2022 Because deep foundation pits and tunnels are deformation-sensitive structures, the safety of these projects is generally affected by coupled risks. In deep foundation pit construction, if the existing tunnel structure adjacent to the deposit is damaged ...
Jie Jiang, Guangyang Liu, Xiaoduo Ou
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Reliability based rehabilitation of water distribution networks by means of Bayesian networks
Journal of Water and Land Development, 2017 Water plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN). The main elements of water distribution network (
Lakehal Abdelaziz, Laouacheria Fares
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