Geometric fluid approximation for general continuous-time Markov chains.

ABSTRACT: 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 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 proce

SUBMITTER: Michaelides M 

PROVIDER: S-EPMC6784392 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature