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On Information-Theoretic Characterizations of Markov Random Fields and Subfields

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 نشر من قبل Raymond Yeung
 تاريخ النشر 2016
  مجال البحث الهندسة المعلوماتية
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Let $X_i, i in V$ form a Markov random field (MRF) represented by an undirected graph $G = (V,E)$, and $V$ be a subset of $V$. We determine the smallest graph that can always represent the subfield $X_i, i in V$ as an MRF. Based on this result, we obtain a necessary and sufficient condition for a subfield of a Markov tree to be also a Markov tree. When $G$ is a path so that $X_i, i in V$ form a Markov chain, it is known that the $I$-Measure is always nonnegative and the information diagram assumes a very special structure Kawabata and Yeung (1992). We prove that Markov chain is essentially the only MRF such that the $I$-Measure is always nonnegative. By applying our characterization of the smallest graph representation of a subfield of an MRF, we develop a recursive approach for constructing information diagrams for MRFs. Our work is built on the set-theoretic characterization of an MRF in Yeung, Lee, and Ye (2002).



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