  We propose parameter optimization techniques for weighted ensemble sampling
of Markov chains in the steady-state regime. Weighted ensemble consists of
replicas of a Markov chain, each carrying a weight, that are periodically
resampled according to their weights inside of each of a number of bins that
partition state space. We derive, from first principles, strategies for
optimizing the choices of weighted ensemble parameters, in particular the
choice of bins and the number of replicas to maintain in each bin. In a simple
numerical example, we compare our new strategies with more traditional ones and
with direct Monte Carlo.
