  There is a great need for improved statistical sampling in a range of
physical, chemical and biological systems. Even simulations based on correct
algorithms suffer from statistical error, which can be substantial or even
dominant when slow processes are involved. Further, in key biomolecular
applications, such as the determination of protein structures from NMR data,
non-Boltzmann-distributed ensembles are generated. We therefore have developed
the "black-box" strategy for re-weighting a set of configurations generated by
arbitrary means to produce an ensemble distributed according to any target
distribution. In contrast to previous algorithmic efforts, the black-box
approach exploits the configuration-space density observed in a simulation,
rather than assuming a desired distribution has been generated. Successful
implementations of the strategy, which reduce both statistical error and bias,
are developed for a one-dimensional system, and a 50-atom peptide, for which
the correct 250-to-1 population ratio is recovered from a heavily biased
ensemble.
