QUANTITATIVE COARSE-GRAINING OF MARKOV CHAINS
Coarse-graining techniques play a central role in reducing the complexity of stochastic models and are typically characterized 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 analyze an effective dynamics, which approximates the dyn
