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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018
摘要
Depending on context, the term 熵 is used for a 熱力學 quantity, a measure of available choice, a quantity to measure 資訊, or, in the context of 統計 inference, a maximum configuration predictor. For systems in equilibrium or processes without memory, the 數學 expression for these different concepts of 熵 appears to be the so-called Boltzmann-Gibbs-Shannon 熵, H. For processes with memory, such as driven- or self- reinforcing-processes, this is no longer true: the different 熵 concepts lead to distinct functionals that generally differ from H. Here we focus on the maximum configuration 熵 (that predicts empirical distribution functions) in the context of driven dissipative systems. We develop the corresponding framework and derive the 熵 functional that describes the distribution of observable states as
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