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原文連結
論文資訊
- 類型：已發表論文
- 日期：2003
摘要
We develop 資訊-theoretic measures of spatial structure and pattern in more than one dimension. As is well known, the 熵 density of a two-dimensional configuration can be efficiently and accurately estimated via a converging sequence of conditional entropies. We show that the manner in which these conditional entropies converge to their asymptotic value serves as a measure of global correlation and structure for spatial systems in any dimension. We compare and contrast 熵 convergence with mutual-資訊 and structure-factor techniques for quantifying and detecting spatial structure.
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