From the 1 of 9 linked papers with an AI index.
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Structural identifiability of partially-observed stochastic processes: from single-particle trajectories to total particle density data
Arianna Ceccarelli, Alexander P. Browning, Ruth E. Baker
The paper presents a method to assess structural identifiability of stochastic process models, showing that parameters can be uniquely recovered from single-particle trajectory dat…
Reliable model selection in the presence of parameter non-identifiability
Yong See Foo, Torkel E. Loman, Alexander P. Browning +3
Mathematical models are invaluable for understanding and predicting how biological systems behave, although their construction requires specifying mechanisms and relationships that…
Exact identifiability analysis for a class of partially observed near-linear stochastic differential equation models
Alexander P Browning, Michael J Chappell, Hamid Rahkooy +2
Stochasticity plays a key role in many biological systems, necessitating the calibration of stochastic mathematical models to interpret associated data. For model parameters to be…
A likelihood-based Bayesian inference framework for the calibration of and selection between stochastic velocity-jump models
Arianna Ceccarelli, Alexander P. Browning, Tai Chaiamarit +2
Advances in experimental techniques allow the collection of high-resolution spatio-temporal data that track individual motile entities. These tracking data can be used to calibrate…