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  • EM Algorithm

    Author(s)
    McLachlan, GJ
    Ng, Shu Kay
    Nguyen, HD
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2021
    Metadata
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    Abstract
    We supplement the article of Meng (2006) on the EM algorithm and its applications, providing also an update on its more recent developments and applications. The expectation–maximization algorithm, popularly known as the EM algorithm, is a general-purpose algorithm for maximum-likelihood estimation in a wide variety of situations best described as incomplete-data problems. The name EM algorithm was given by Dempster et al. (1997) in a celebrated paper read before the Royal Statistical Society in 1976 and published in its journal in 1977.We supplement the article of Meng (2006) on the EM algorithm and its applications, providing also an update on its more recent developments and applications. The expectation–maximization algorithm, popularly known as the EM algorithm, is a general-purpose algorithm for maximum-likelihood estimation in a wide variety of situations best described as incomplete-data problems. The name EM algorithm was given by Dempster et al. (1997) in a celebrated paper read before the Royal Statistical Society in 1976 and published in its journal in 1977.
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    Book Title
    Wiley StatsRef: Statistics Reference Online (WSR)
    DOI
    https://doi.org/10.1002/9781118445112.stat00410.pub2
    Funder(s)
    ARC
    Grant identifier(s)
    DP170100907
    Subject
    Statistics
    Publication URI
    http://hdl.handle.net/10072/414484
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    • Book chapters

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