S. Bacallado, J. Chodera, and V. Pande. Journal of Chemical Physics 131, 045106 (2009).
Markov State Models (MSMs) are one of the most common ways to analyze Folding@Home simulations. This paper introduces a new validation method, which could play an important role in automating their construction
Discrete-space Markov models are a convenient way of describing the
kinetics of biomolecules. The most common strategies used to validate
these models employ statistics from simulation data, such as the
eigenvalue spectrum of the inferred rate matrix, which are often
associated with large uncertainties. Here, we propose a Bayesian
approach, which makes it possible to differentiate between models at a
fixed lag time making use of short trajectories. The hierarchical
definition of the models allows one to compare instances with any
number of states. We apply a conjugate prior for reversible Markov
chains, which was recently introduced in the statistics literature.
The method is tested in two different systems, a Monte Carlo dynamics
simulation of a two-dimensional model system a