  Although Monte Carlo calculations using Importance Sampling have matured into
the most widely employed method for determining first principle results in QCD,
they spectacularly fail for theories with a sign problem or for which certain
rare configurations play an important role. Non-Markovian Random walks, based
upon iterative refinements of the density-of-states, overcome such overlap
problems. I will review the Linear Logarithmic Relaxation (LLR) method and, in
particular, focus onto ergodicity and exponential error suppression.
Applications include the high-state Potts model, SU(2) and SU(3) Yang-Mills
theories as well as a quantum field theory with a strong sign problem: QCD at
finite densities of heavy quarks.
