If the timescales remain relatively unchanged for a series of τ values and higher, then the models constructed for those values of τ can be considered Markovian. The values of P ij can then be estimated using a variety of estimation methods . A critical component of high-quality MSM construction is the features selected in the discretization of state space. It is important to balance statistical error against systematic bias when choosing, as narrow features ranges may result in poor statistics, while broadly-ranged features may have large systematic errors due to convolution of multiple motions into a single state. Choice of appropriate features can be challenging due to the sheer size of simulation datasets and the system-specific knowledge often need to identify appropriate features.