THE EM ALGORITHM FOR GRAPHICAL ASSOCIATION MODELS WITH MISSING DATA

被引：476

作者：

LAURITZEN, SL ^{[1
]}

机构：

[1] AALBORG UNIV,INST ELECTR SYST,DEPT MATH & COMP SCI,DK-9220 AALBORG O,DENMARK

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 1995年 / 19卷 / 02期

关键词：

CONTINGENCY TABLES; BELIEF NETWORKS; DECOMPOSABLE MODELS; EXPERT SYSTEMS; HIERARCHICAL MODELS; LATENT VARIABLES; PROBABILITY PROPAGATION; RECURSIVE MODELS;

D O I：

10.1016/0167-9473(93)E0056-A

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

It is shown how the computational scheme of Lauritzen and Spiegelhalter (1988) can be exploited to perform the E-step of the EM algorithm when applied to finding maximum likelihood estimates or penalized maximum likelihood estimates in hierarchical log-linear models and recursive models for contingency tables with missing data. The generalization to mixed association models introduced in Lauritzen and Wermuth (1989) and Edwards (1990) is indicated.

引用

页码：191 / 201

页数：11

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