  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 non-equilibrium 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 one-dimensional
double-well potential, and to a 208$\mu$s atomistic Trp-cage folding trajectory
from D.E. Shaw Research.
