  In many complex statistical models maximum likelihood estimators cannot be
calculated. In the paper we solve this problem using Markov chain Monte Carlo
approximation of the true likelihood. In the main result we prove asymptotic
normality of the estimator, when both sample sizes (the initial and Monte Carlo
one) tend to infinity. Our result can be applied to models with intractable
norming constants and missing data models.
