5 citations · 11 across the 3 of their papers we have counts for
3 papers
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…
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…
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…