Maximum Likelihood
نویسندگان
چکیده
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 eecient way to compute the E{step. The main purpose of the paper is to show that for certain missing patterns the computational eeort can considerably be reduced.
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