Mathematical modelling plays a key role in achieving many scientific objectives. BioSS aims to enhance this role by addressing generic issues including: simplification, analysis and approximation of models for complex systems; parameter estimation and model selection in stochastic process models; Bayesian methods for decision support; and methodologies for estimating risks in complex interacting systems.
Much of the mathematical modelling work at BioSS involves the development of continuous time models based on ordinary, delay and stochastic differential equations for a range of applications.
Bayesian Inference for stochastic processes
By comparing models with data Bayesian inference provides a framework to address two critical modelling challenges: (i) what values to assign parameters; and (ii) which processes to include.
Large-scale and Systems Modelling
Biodiversity and Ecosystem Tools
Sustainable Agriculture Tools
RESAS-funded work which cuts across all methodological themes