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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011
摘要
Appealing to several multivariate 資訊 measures - some familiar, some new here-we analyze the 資訊 embedded in discrete-valued 隨機 time series. We dissect the uncertainty of a single observation to demonstrate how the measures' asymptotic behavior sheds structural and semantic light on the generating process's internal 資訊 dynamics. The measures scale with the length of time window, which captures both intensive (rates of growth) and subextensive components. We provide interpretations for the components, developing explicit relationships between them. We also identify the 資訊al component shared between the past and the future that is not contained in a single observation. The existence of this component directly motivates the notion of a process's effective (internal) states and indicates why one
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