Inference and prediction for stochastic models of biological populations undergoing migration and proliferation | Journal of The Royal Society Interface | The Royal Society

Matthew J. Simpson, Michael J. Plank; Inference and prediction for stochastic models of biological populations undergoing migration and proliferation. J R Soc Interface 1 October 2025; 22 (231): 20250536. https://doi.org/10.1098/rsif.2025.0536 Download citation file: Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability and model prediction are well developed for deterministic continuum models, working with biological applications often requires stochastic modelling approaches to capture inherent variability and randomness that can be prominent in biological measurements and data. Random walk models are especially useful for capturing spatio-temporal processes, such as ecological population dynamics, molecular transport phenomena and collective behaviour associated w

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