ABSTRACT
The “expectation maximization” (EM) algorithm is a general framework for maximizing the likelihood function in statistical parameter estimation [167]. The EM algorithm is not really a single algorithm, but a framework for the design of iterative likelihood maximization methods, or, as the authors of [21] put it, a “prescription for constructing an algorithm”; nevertheless, we shall continue to refer to the EM algorithm. We show in
EM are always presented in probabilistic terms, involving the maximization of a conditional expected value. As we shall demonstrate, the essence of the EM algorithm is not stochastic. Our non-stochastic EM (NSEM) is a general approach for function maximization that has the stochastic EM methods as particular cases.
