  We present an efficient Monte Carlo algorithm for determining the density of
states which is based on the statistics of transition probabilities between
states. By measuring the infinite temperature transition probabilities--that
is, the probabilities associated with move proposal only--we are able to
extract excellent estimates of the density of states. When this estimator is
used in conjunction with a Wang-Landau sampling scheme [F. Wang and D. P.
Landau, Phys. Rev. Lett. 86, 2050 (2001)], we quickly achieve uniform sampling
of macrostates (e.g., energies) and systematically refine the calculated
density of states. This approach requires only potential energy evaluations,
continues to improve the statistical quality of its results as the simulation
time is extended, and is applicable to both lattice and continuum systems. We
test the algorithm on the Lennard-Jones liquid and demonstrate good statistical
convergence properties.
