Comparing mathematical models offers a means to evaluate competing scientific
theories. Tuttavia, exact methods of model calibration are not applicable to
many probabilistic models which simulate high-dimensional spatio-temporal data.
Approximate Bayesian Computation is a widely-used method for parameter
inference and model selection in such scenarios, and it may be combined with
Topological Data Analysis to study models which simulate data with fine spatial
structure. We develop a flexible pipeline for parameter inference and model
selection in spatio-temporal models. Our pipeline identifies topological
summary statistics which quantify spatio-temporal data and uses them to
approximate parameter and model posterior distributions. We validate our
pipeline on models of tumour-induced angiogenesis, inferring four parameters in
three established models and identifying the correct model in synthetic
test-cases.

Questo articolo esplora i giri e le loro implicazioni.

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