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  3. Mathematik, Informatik und Statistik - Open Access LMU - Teil 01/03 Podcast
  4. Maximum Likelihood Estimation in Graphical Models with Missing Values
In this paper we discuss maximum likelihood estimation when some
observations are missing in mixed graphical interaction models
assuming a conditional Gaussian distribution as introduced by
Lauritzen&Wermuth (1989). For the saturated case ML estimation
with missing values via the EM algorithm has been proposed by
Little&Schluchter (1985). We expand their results to the
special restrictions in graphical models and indicate a more
efficient way to compute the E--step. The main purpose of the paper
is to show that for certain missing patterns the computational
effort can considerably be reduced.

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