ETAPS 2019
Sat 6 - Thu 11 April 2019 Prague, Czech Republic
Sun 7 Apr 2019 15:00 - 15:30 at S9 - VII Chair(s): David Safranek

Fluid approximations have been greatly successful in approximating the macro-scale behaviour of Markov systems with a large number of discrete states. However, these methods rely on the continuous-time Markov chain (CTMC) having a particular population structure which suggests a natural continuous state-space endowed with a dynamics for the approximating process.

We construct here a general method based on spectral analysis of the transition matrix of the CTMC, without the need for a population structure. Specifically, we use the popular manifold learning method of diffusion maps to analyse the transition matrix as the operator of a hidden continuous process. An embedding of states in a continuous space is recovered, and the space is endowed with a drift vector field inferred via Gaussian process regression. In this manner, we construct an ODE whose solution approximates the evolution of the CTMC mean, mapped onto the continuous space (known as the fluid limit).

Sun 7 Apr

14:00 - 15:30: HSB - VII at S9
Chair(s): David SafranekMasaryk University
hsb-2019-papers14:00 - 15:00
Igor SchreiberUniversity of Chemistry and Technology of Prague
hsb-2019-papers15:00 - 15:30
Michalis MichaelidesUniversity of Edinburgh, Jane HillstonUniversity of Edinburgh, Guido SanguinettiUniversity of Edinburgh