Traditional single-matrix Markov state models are biased even with infinite data; unbiased coarse-grained observables require separate equilibrium and A-to-B nonequilibrium transition matrices built from stationary within-cluster sampling.
What Markov state models can and cannot do: Correlation versus path-based observables in protein-folding models
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Markov state models revisited: Principles and algorithms for unbiased observables
Traditional single-matrix Markov state models are biased even with infinite data; unbiased coarse-grained observables require separate equilibrium and A-to-B nonequilibrium transition matrices built from stationary within-cluster sampling.