The EM Algorithm: A Guided Tour
Christophe Couvreur · Birkhäuser Boston eBooks · 1997
The Expectation-Maximization (EM) algorithm has become one of the methods of choice for maximum-likelihood (ML) estimation. In this tutorial paper, the basic principles of the algorithm are described in an informal fashion and illustrated on a notional example. Various applications to real-world problems are briefly presented. We also provide selected entry points to the vast literature on the EM algorithm for the reader interested in a rigorous mathematical treatment and further details on the applications. We discuss the convergence properties of the algorithm and review some variants and improvements that have been proposed. We conclude by some practical advice for the practicing engineer interested in the implementation of the EM algorithm.