1,237 research outputs found
Graphical Log-linear Models: Fundamental Concepts and Applications
We present a comprehensive study of graphical log-linear models for
contingency tables. High dimensional contingency tables arise in many areas
such as computational biology, collection of survey and census data and others.
Analysis of contingency tables involving several factors or categorical
variables is very hard. To determine interactions among various factors,
graphical and decomposable log-linear models are preferred. First, we explore
connections between the conditional independence in probability and graphs;
thereafter we provide a few illustrations to describe how graphical log-linear
model are useful to interpret the conditional independences between factors. We
also discuss the problem of estimation and model selection in decomposable
models
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