We present information-theoretic approach to parameterisation of coarse-grained dynamics defined as continuous time Markov chains. Rates of the coarse-grained process are parametrized and optimal parameters are selected by minimization of the relative entropy on the path space. This approach extends techniques also known as inverse Monte Carlo to models with non-equilibrium stationary states, for example systems driven by external parameters or reaction-diffusion systems in catalysis.
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