An Application of Dynamic Bayesian Networks to Model Regime Shifts and Changepoint Processes

An Application of Dynamic Bayesian Networks to Model Regime Shifts and Changepoint Processes

Presented at the 2024 BayesiaLab Conference in Cincinnati on April 12, 2024.


Understanding the dynamics that regulate ecological resilience is becoming increasingly important in today’s world, as ecosystems face multiple global, regional, and local pressures. If pressures exceed a threshold, this may trigger a regime shift, where a system undergoes a step change to another state that can last for substantial periods of time. However, modeling such change is not simple as ecological data is scarce, and models often assume that relationships within ecosystems remain homogenous over time. In this talk, we document the application of non-homogenous Dynamic Bayesian Networks to various complex systems known to have undergone major structural changes.

Presentation Video

About the Presenter

Edwin Hui is a Ph.D. student from the University of St Andrews, where his research focuses on developing computational models to study resilience and regime shifts across complex systems. He is interested in applying a variety of statistical and computational tools to address ecological questions and study complex systems theory. Throughout his Ph.D., he aims to develop novel computational approaches to study complex systems across different disciplines, ranging from ecological to macroeconomic systems.

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