EMアルゴリズムの基礎 Fundamentals of the EM Algorithm

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The EM (Expectation-Maximization) algorithm is a general-purpose stable procedure for maximum likelihood estimation in a wide variety of situations described as incomplete-data problem. Incomplete- data problems where the EM algorithm has been succesfully applied include not only evidently incomplete- data situations, for example, there are missing data, grouped observations, but also a whole variety of situations where the incompleteness of the data is not natural or evident.<BR>In this article, at first, I summarize maximum likelihood estimation and formulation of the EM algorithm. Subsequently, I briefly mention the properties of the EM algorithm, and two applications where the typical probablistic models are assumed. Lastly, I introduce some problems, which arise from applying the EM algorithm to the complex situations, and the examples of the solutions against them.

収録刊行物

  • 放射線医学物理  

    放射線医学物理 19(3), 148-161, 1999-09-30 

    Japan Society of Medical Physics

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各種コード

  • NII論文ID(NAID)
    10004572550
  • NII書誌ID(NCID)
    AA11137895
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    09188010
  • データ提供元
    CJP書誌  J-STAGE 
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