Iterative trajectory reweighting for estimation of equilibrium and nonequilibrium observables
Abstract
We present two algorithms by which a set of short, unbiased trajectories can be iteratively reweighted to obtain various observables. The first algorithm estimates the stationary (steady state) distribution of a system by iteratively reweighting the trajectories based on the average probability in each state. The algorithm applies to equilibrium or nonequilibrium steady states, exploiting the `left' stationarity of the distribution under dynamics  i.e., in a discrete setting, when the column vector of probabilities is multiplied by the transition matrix expressed as a left stochastic matrix. The second procedure relies on the `right' stationarity of the committor (splitting probability) expressed as a row vector. The algorithms are unbiased, do not rely on computing transition matrices, and make no Markov assumption about discretized states. Here, we apply the procedures to a onedimensional doublewell potential, and to a 208$\mu$s atomistic Trpcage folding trajectory from D.E. Shaw Research.
 Publication:

arXiv eprints
 Pub Date:
 June 2020
 arXiv:
 arXiv:2006.09451
 Bibcode:
 2020arXiv200609451R
 Keywords:

 Physics  Computational Physics;
 Quantitative Biology  Quantitative Methods;
 Statistics  Computation