Before revision:  Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining Markov chains---as well as a more general class of probabilistic models---making them more comprehensible while remaining as faithful as possible to the original kinetics by accounting for model uncertainty .
After revision:  Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining Markov chains---as well as a more general class of probabilistic models---making them more comprehensible . An important feature of this algorithm is its ability to explicitly account for statistical uncertainty in model parameters that arises from finite sampling. This advance builds on a number of recent works highlighting the importance of accounting for uncertainty in the analysis of MSMs and provides significant advantages over existing methods for coarse-graining Markov state models .