S2.2 Bayesian Interference Techniques

28 Sep 2018
10:30 - 12:00
Lecture Hall 4

S2.2 Bayesian Interference Techniques

S2.2 _ Why Bayesian? Integrating environmental modelling with Bayesian inference techniques. (Session Chairs: George Arhonditsis, Alex Neumann and Yuko Shimoda)

The credibility of the scientific methodology of environmental models and their adequacy to form the basis of public policy decisions have been frequently challenged. The current challenges make compelling the development of more realistic modeling platforms (i) to elucidate causal mechanisms, complex interrelationships, direct and indirect ecological paths; (ii) to examine the interactions among the various stressors (e.g., climate change, urbanization/land‐use changes, alternative management practices, invasion of exotic organisms); and (iii) to assess their potential consequences on ecosystem functioning. The proposed session aims to provide insights into the current state of the field, and also highlight the major challenges and future directions of research. Special emphasis will be placed on studies that address topics, such as novel uncertainty analysis techniques, Bayesian inference methods (including Bayesian networks), development of new model formulations and proper representation of biotic functional types, emerging techniques of data assimilation and model optimization, effective integration of physics with biology, and strategies to improve the contribution of complex models to ecological theories. The proposed session encourages contributions from both mathematical and statistical ecosystem modelers.

Keywords: bayesian inference, uncertainty analysis, environmental management, policy analysis

Talk 06-11

Integrating Hierarchical Bayes with Limnological Modelling
Yuko Shimoda and George Arhonditsis

Predicting the spatial and temporal dynamics of hypoxia in Hamilton Harbour, Ontario, Canada: A Bayesian modelling framework
Dong-Kyun Kim, George B. Arhonditsis

Overview of Bayesian inference techniques for conceptual semi-empirical watershed models (SPARROW, GREEN)
Alex Neumann, Dong-Kyun Kim, Feifei Dong and George Arhonditsis

Application of BBN-models to link aquatic invertebrate traits to environmental river conditions in the Guayas basin (Ecuador)
Marie Anne Eurie Forio, Wout Van Echelpoel, Niels De Troyer, Arne De Knock, Luis Dominguez and Peter Goethals

Modeling a Species Identification Process as a Bayesian Inference Problem
Oliver Bley and Patrick Mäder

Evaluating explanations of land-cover change in the Iberian agricultural revolution using approximate Bayesian computation
Andrew Lane, James Millington, Simon Miles