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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Veröffentlicht in:	Journal of the Royal Statistical Society Series C: Applied Statistics 2018-02, Vol.67 (2), p.417-434
Hauptverfasser:	Wu, Paul P.-Y., Caley, M. Julian, Kendrick, Gary A., McMahon, Kathryn, Mengersen, Kerrie
Format:	Artikel
Sprache:	eng
Schlagworte:	Absorption Bayesian analysis Complex system Complex systems Computing time Convergence Decision makers Dredging Dynamic Bayesian network Ecosystem modelling Ecosystems Grasses Markov chains Non‐homogeneous Markov chain Probabilistic inference Seagrass Strategic management Transition probabilities
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