From the 1 of 9 linked papers with an AI index.
2 citations · 2 across the 4 of their papers we have counts for
9 papers
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…
Structural functional identifiability and model discovery in differential equation models
Torkel E Loman, Alexander P Browning, Ruth E Baker
Differential equation models are widely used to describe, interpret, and predict dynamical phenomena across science and engineering. In practice, however, the governing dynamics ar…
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…
Framing structural identifiability in terms of parameter symmetries
Johannes G Borgqvist, Alexander P Browning, Fredrik Ohlsson +1
A key step in mechanistic modelling of dynamical systems is to conduct a structural identifiability analysis. This entails deducing which parameter combinations can be estimated fr…
Framing local structural identifiability and observability in terms of parameter-state symmetries
Johannes G. Borgqvist, Alexander P. Browning, Fredrik Ohlsson +1
We introduce a subclass of Lie symmetries, called parameter-state symmetries, to analyse the local structural identifiability and observability of mechanistic models consisting of…
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…