3 papers
eess.SY2019
Geometric fluid approximation for general continuous-time Markov chains
Michalis Michaelides, Jane Hillston, Guido Sanguinetti
Fluid approximations have seen great success in approximating the macro-scale behaviour of Markov systems with a large number of discrete states. However, these methods rely on the…
q-bio.QM2017
Statistical abstraction for multi-scale spatio-temporal systems
Michalis Michaelides, Jane Hillston, Guido Sanguinetti
Spatio-temporal systems exhibiting multi-scale behaviour are common in applications ranging from cyber-physical systems to systems biology, yet they present formidable challenges f…
eess.SY2016
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Michalis Michaelides, Dimitrios Milios, Jane Hillston +1
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to…