Process & Systems Modelling

Mathematical modelling is vital for understanding and predicting how different processes in any particular system combine to determine the full system behaviour.


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.


Dynamic Models

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


Helen Kettle is the Principal Researcher in Process & Systems Modelling.

Lead contributors to this research area are:

 Stephen Catterall  Dave Ewing 

 Giles Innocent  Helen Kettle 

Martin Knight  Glenn Marion  

Katharine Preedy Chris Pooley 

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