Title:
Gupta-and-Chen---2010---Theory
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Description: This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs), and estimat-
ing hidden Markov models (HMMs). EM solutions are also derived
for learning an optimal mixture of fi xed models, for estimating the
parameters of a compound Dirichlet distribution, and for dis-entangling
superimposed signals. Practical issues that arise in the use of EM are
discussed, as well as variants of the algorithm that help deal with these
challenges.
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