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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Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series [PDF]
, 2011 To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level.
Dondelinger, F. +8 more
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Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks [PDF]
, 2009 Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be ...
Husmeier, D. +3 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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On Causal Explanations in Bayesian Networks [PDF]
, 2007 Explanations in Bayesian networks are usually probabilistic measures of how well a hypothesis is supported by observations. This observational based approach does not fulfill all the properties one would expect from an explanation. In particular, it does
Nielsen, Ulf Holm
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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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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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Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes [PDF]
, 2010 <b>Method:</b> Dynamic Bayesian networks (DBNs) have been applied widely to reconstruct the structure of regulatory processes from time series data, and they have established themselves as a standard modelling tool in computational systems ...
Husmeier, D. +5 more
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Dynamic staged trees for discrete multivariate time series : forecasting, model selection and causal analysis [PDF]
, 2010 A new tree-based graphical model — the dynamic staged tree — is used to model discrete-valued discrete-time multivariate processes which are hypothesised to exhibit certain symmetries concerning how situations might unfold.
Smith, JQ +5 more
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