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Identifiability of Stochastic and Spatial Models in Mathematical Biology
Effective application of mathematical models to interpret biological data and make accurate predictions often requires that model parameters are identifiable. Requisite to identifiability from a finite amount of noisy data is that model parameters are first structurally identifiable: a mathematical question that establishes whether multiple parameter values may give rise to indistinguishable model outputs. Approaches to assess structural identifiability of deterministic ordinary differential equation models are well-established, however tools for the assessment of the increasingly relevant stochastic and spatial models remain in their infancy.
I provide in this talk an introduction to structural identifiability, before presenting new frameworks for the assessment of stochastic and partial differential equations. Importantly, I discuss the relevance of our methodology to model selection, and more the practical and aptly named practical identifiability of parameters in the context of experimental data. Finally, I conclude with a brief discussion of future research directions and remaining open questions.
Date:
6 March 2026, 11:00
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Venue Details:
L4
Speaker:
Alex Browning (University of Melbourne)
Organising department:
Mathematical Institute
Organiser contact email address:
sara.jolliffe@maths.ox.ac.uk
Host:
Giulia Celora (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Sara Jolliffe
