文件名称:Gupta-and-Chen---2010---Theory
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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.,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.
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.,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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