Giovedì, 31 Maggio 2018
Aula B, via Alfonso Corti 12, Milano, Italy
Non-homogeneous Inference Using Dynamic Bayesian Networks: Minimising the Impact of Dredging on Seagrass Ecosystems
Il Dott. Wu è uno dei due relatori, insieme alla Prof.ssa Kerrie Mengersen (QUT), della scuola estiva ABS18 (Applied Bayesian Statistics) su
BAYESIAN STATISTICAL MODELLING AND ANALYSIS IN SPORT che si terrà la prossima settimana a Como e per cui sono ancora
disponibili alcuni posti: http://www.mi.imati.cnr.it/conferences/abs18/index.html
It is challenging to predict the dynamic response of a complex system to stressors due to interdependencies and interactions between multiple system components under uncertainty. Potentially, the behaviour of the system itself can change over time as a result of exogeneous inputs and/or changes to the system state. For instance, an ecosystem that has already been subjected to stress may respond differently to further stresses, such as a reduced ability to resist and recover. Such changing dynamics are characteristic of non-homogeneous complex systems.
Dynamic Bayesian Networks (DBNs) provide an approach for predictive, whole-of-systems modelling of complex systems under uncertainty. Here, we discuss an approach to non-homogeneous inference with DBNs. The method enables dynamic updates of DBN parameters and inference of posterior marginal probabilities by propagating the effect of observations forwards in time. It also enables approximate inference for forwards-backwards inference. The approach is demonstrated on evaluating dredging impacts on seagrass ecosystems, with discussion on broader application to other domains.