David James Warne

The paper assesses whether standard ecological data can identify bistability and tipping points in ecosystems using synthetic data and profile likelihood analysis.

This paper develops a framework to assess explainable ML sensitivity by comparing consensus across model architectures, using synthetic coral data to demonstrate its utility in ecological modeling.

The paper introduces parameter-wise prediction for stochastic random walk models in life sciences, using PDE surrogates and likelihood-based methods to analyze sensitivity and parameter estimation.

This paper introduces a Bayesian hierarchical framework to quantify both inter- and intra-experimental heterogeneity in dynamical processes using differential equations, demonstrating its robustness and flexibility.

This paper calibrates agent-based models of tumour growth using approximate Bayesian computation to estimate parameters and quantify uncertainty in biphasic growth models.

The paper develops a computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth using profile likelihood analysis.

This paper analyzes identifiability in stochastic differential equation models using case studies and synthetic data, showing they can extract more parameter information than deterministic models.