  We consider Monte Carlo approximations to the maximum likelihood estimator in
models with intractable norming constants. This paper deals with adaptive Monte
Carlo algorithms, which adjust control parameters in the course of simulation.
We examine asymptotics of adaptive importance sampling and a new algorithm,
which uses resampling and MCMC. This algorithm is designed to reduce problems
with degeneracy of importance weights. Our analysis is based on martingale
limit theorems. We also describe how adaptive maximization algorithms of
Newton-Raphson type can be combined with the resampling techniques. The paper
includes results of a small scale simulation study in which we compare the
performance of adaptive and non-adaptive Monte Carlo maximum likelihood
algorithms.
