Dynamic Bayesian network inferencing for non-homogeneous complex systems

Paul P.Y. Wu, M. Julian Caley, Gary A. Kendrick, Kathryn McMahon, Kerrie Mengersen

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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 be modelled especially for complex ecosystems that are susceptible to regime change (i.e. change in state transition probabilities). Such regime changes can occur as a result of exogenous inputs and/or because of past system states; the latter is known as path dependence. We develop a method for non-homogeneous DBN inference to capture the dynamics of potentially path-dependent ecosystems. The method enables dynamic updates of DBN parameters at each time slice in computing posterior marginal probabilities given evidence for forward inference. An approximate algorithm for forward–backward inference is also provided noting that convergence is not guaranteed in a path-dependent system. We demonstrate the methods on a seagrass dredging case-study and show that the incorporation of path dependence enables conditional absorption into and release from the zero state in line with ecological observations. The model helps managers to develop practical ways to manage the marked effects of dredging on high value seagrass ecosystems.

Original languageEnglish
Pages (from-to)417-434
Number of pages18
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number2
Publication statusPublished - 1 Feb 2018


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