Dynamic Models in R: Programming, parameter estimation and model selection

Ecological modelling, based on field data, has become an indispensable tool in ecological research. It consists of a number of steps: analysing data, proposing plausible mechanistic models and mechanistic explanations for observed phenomena, and selecting models based on maximum likelihood or an information criterion. This course presents a conceptual framework for ecological modelling: covering elementary growth models and probability distributions needed to mathematically model processes. The models are confronted with the data, using state of the art statistical methods. The course presents techniques for dynamic simulation, model fitting, parameter estimation, and model selection based on maximum likelihood and information theory. While the theory has emerged from the field of ecology, it has shown to be widely applicable in the life sciences. The course is taught with R as the programming language because it is freeware and it allows flexibility in handling and modelling data.

Former occurrences of this course

8 March – 12 April 2019   |   10 March – 7 April 2017