  Adaptive Monte Carlo methods can be viewed as implementations of Markov
chains with infinite memory. We derive a general condition for the convergence
of a Monte Carlo method whose history dependence is contained within the
simulated density distribution. In convergent cases, our result implies that
the balance condition need only be satisfied asymptotically. As an example, we
show that the adaptive integration method converges.
