The entire process of transforming MD frames into a transition matrix involves making a number of modeling choices called hyperparameters. Hyperparameters are differentiated from the parameters of the model because the latter are calculated from the data via the optimization of a loss function (e.g., the negative log-likelihood), while the hyperparameters are chosen via expert judgment, or via some summary metric of the model. For MSMs, the important hyperparameters are which subset of atoms from the simulation to include (e.g., a protein loop, pocket, or other substructure of interest); the transformation of these coordinates into important features (e.g., residue-residue distances, backbone dihedral angles); dimensionality reduction onto a set of important collective variables (typically timelagged independent component analysis, TICA, is used for this purpose); and finally how to define discrete states from these collective variables (via some clustering algorithm such as K-Means). Therefore, the parameters of an MSM are the conditional probabilities in the transition matrix, T ij , whereas the hyperparameters are all the choices (choice of features, clustering algorithm, etc.) that gave us the specific state definitions used in the likelihood maximization step.