Speaker
Description
Pattern formation arising from the collective behaviour of autonomous agents occurs across many areas of biology, including skin patterns. Agent-based models provide a natural framework for describing such systems. However, the high-dimensional nature of the data and model stochasticity pose significant challenges for parameter inference and identifiability analysis. To help address this challenge, researchers often rely on lower-dimensional summaries of model output, such as cell number or stripe width. However, it remains unclear which quantitative summaries are most informative for inference. In this work, we compare topological signatures derived from Vietoris–Rips and sweeping-plane filtrations with classical statistical summaries, such as pair correlation functions. We evaluate the effectiveness of these different quantitative approaches within a Bayesian pipeline for parameter inference, and show how the choice of method for summarizing pattern data impacts parameter identifiability.
