The MSM approach assumes that a trajectory is generated from a Markov process. This assumption allows a statistical interpretation of MD trajectories. Specifically, a Markov State Model (MSM) is a matrix containing the transition probabilities between defined microstates, which can be used to predict the long timescale behavior of a system. The resulting model approximates the temporally coarse-grained dynamics with a Markovian surrogate model, which has certain properties that can used to predict the kinetics and equilibrium populations of each state. Because a MSM is concerned only with the transitions between states, multiple simulations can be used to generate the model, leading to more efficient use of computational resources. Specifically, rather than running a single very long simulation to adequately sample all binding modes, many shorter simulations can be used with substantially less wallclock time, at least if parallel resources are available. The MSM framework also works to predict the long-timescale behavior of a system even before global equilibrium is reached, as long as local equilibrium is achieved, allowing a smaller total amount of simulation time to be used to estimate the equilibrium populations of all states rather than having to fully converge to the global equilibrium. Sampling at different thermodynamic states can also be employed with MSMs to improve transition estimates and sampling. However, the mathematics and assumptions behind MSMs unfortunately make this method difficult to use without expert knowledge and considerable care, and a maximum increase in efficiency is obtained only with prior knowledge of all potential binding modes. A sufficient number of transitions between states is necessary to properly estimate equilibrium populations from the MSM robustly. It is difficult to know a priori how much simulation data will be required to reach this stage, and it can require careful checking to know when this has been achieved. There are also many parameter choices (order parameters, lagtimes, clustering methods) which make constructing MSMs difficult to generalize, although recent developments such as the generalized matrix Raleigh quotient and timelagged independent component analysis (tICA) help to reduce dependence on parameter choices.