On simulated EM algorithms

The EM algorithm is a popular and useful algorithm for finding the maximum likelihood estimator in incomplete data problems. Each iteration of the algorithm consists of two simple steps: an E-step, in which a conditional expectation is calculated, and an M-step, where the expectation is maximized. In some problems, however, the EM algorithm cannot be applied since the conditional expectation required in the E-step cannot be calculated. Instead the expectation may be estimated by simulation. We call this a simulated EM algorithm. The simulations can, at least in principle, be done in two ways. Either new independent random variables are drawn in each iteration, or the same uniforms are re-used in each iteration. In this paper the properties of these two versions of the simulated EM algorithm are discussed and compared.