The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Theory and Use of the EM Algorithm
Abstract
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 estimating hidden Markov models (HMMs). EM solutions are also derived for learning an optimal mixture of fixed 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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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Bayesian-Based Iterative Method of Image Restoration*
Richardson, W. H. (1972) · Journal of the Optical Society of America · doi:10.1364/josa.62.000055 · Cited by 4,160
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866 · cited 1×
“Another notable instance of a special case of the EM algorithm is the Richardson–Lucy image deconvolution of the early 1970s [51, 33].”
Background
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Sensitivity and specificity of information criteria
(2019) · doi:10.1093/bib/bbz016 · Cited by 718
Metadata
- Authors
- Gupta, M. R.
- Venue
- Foundations and Trends® in Signal Processing
- Publisher
- Emerald
- Published
- 2010
- Volume
- 4
- Issue
- 3
- Pages
- 223-296
- ISSN
- 1932-8346 (ISSN-L)
- 1932-8354
- OpenAlex
- W4292080463
- Cited by
- 272
- References
- 4
- FWCI
- 11.24
- Type
- journal article
- Language
- en
- Open access
- closed
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