Quantitative coarse-graining of Markov chains

TL;DR

This paper introduces a method for approximating the dynamics of coarse-grained Markov chains without requiring scale separation, providing bounds on the approximation error and comparing with existing averaging techniques.

Contribution

It proposes a new effective dynamics for continuous-time Markov chains that accurately approximates the coarse-grained system without explicit scale separation.

Findings

Effective dynamics closely approximates original Markov chain

Quantitative bounds on approximation error are established

Comparison with averaging methods highlights advantages

Abstract

Coarse-graining techniques play a central role in reducing the complexity of stochastic models, and are typically characterised by a mapping which projects the full state of the system onto a smaller set of variables which captures the essential features of the system. Starting with a continuous-time Markov chain, in this work we propose and analyse an effective dynamics, which approximates the dynamical information in the coarse-grained chain. Without assuming explicit scale-separation, we provide sufficient conditions under which this effective dynamics stays close to the original system and provide quantitative bounds on the approximation error. We also compare the effective dynamics and corresponding error bounds to the averaging literature on Markov chains which involve explicit scale-separation. We demonstrate our findings on an illustrative test example.