Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian networks (DBNs) provide a versatile method for predictive, whole-of-systems modelling to support decision makers in managing natural systems subject to anthropogenic disturbances. However, DBNs typically assume a homogeneous Markov chain which we show can limit the dynamics that can...

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Bibliographic Details
Published in:	Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 67; no. 2; pp. 417 - 434
Main Authors:	Wu, Paul P.-Y., Caley, M. Julian, Kendrick, Gary A., McMahon, Kathryn, Mengersen, Kerrie
Format:	Journal Article
Language:	English
Published:	Oxford John Wiley & Sons Ltd 01.02.2018 Oxford University Press
Subjects:	Absorption Bayesian analysis Changes Complex system Complex systems Computing time Convergence Decision makers Decision making Dredging Dynamic Bayesian network Ecosystem modelling Ecosystems Grasses Inference Markov analysis Markov chains Non‐homogeneous Markov chain Path dependence Probabilistic inference Seagrass Strategic management Transition probabilities
ISSN:	0035-9254, 1467-9876
Online Access:	Get full text
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