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Dynamic Bayesian Network Inferencing for Nonhomogenous Complex Systems
25 June 2020 @ 4:00 pm - 5:00 pm
Distinguished Professor Kerrie Mengersen, Queensland University of Technology
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.
In this presentation I will describe a method that we* developed 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.
We demonstrated the methods on a seagrass dredging case-study and showed that the incorporation of path dependence enables conditional absorption into and release from the zero state in line with ecological observations. The model can help managers to develop practical ways to manage the marked effects of dredging on high value seagrass ecosystems.
*This work was led by Paul Wu and is joint with Julian Caley, Gary Kendrick and Kathryn McMahon. The foundation methodology was published in the Journal of the Royal Statistical Society Series C, 67, 417-434.
About the speaker
Distinguished Professor Kerrie Mengersen is a statistician and Director of the Centre for Data Science at QUT. She is an elected Fellow of the Australian Academy of Science, the Australian Social Sciences Academy, and the Queensland Academy of Arts and Sciences.
As an ARC Laureate Fellow, she works on the development and application of methods for using diverse types of data to learn about complex systems and problems in health, the environment and industry. She is currently working with international teams to use data science to gain insights into national and international dynamics of COVID-19.