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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014-04-29
摘要
The maximum 熵 principle (MEP) is a method for obtaining the most likely distribution functions of observables from 統計 systems by maximizing 熵 under constraints. The MEP has found hundreds of applications in 遍歷 and 馬可夫ian systems in 統計 mechanics, 資訊 theory, and statistics. For several decades there has been an ongoing controversy over whether the notion of the maximum 熵 principle can be extended in a meaningful way to nonextensive, non遍歷, and complex 統計 systems and processes. In this paper we start by reviewing how Boltzmann-Gibbs-Shannon 熵 is related to multiplicities of independent random processes. We then show how the relaxation of independence naturally leads to the most general entropies that are compatible with the first three Shannon-Khinchin axioms, the (c,d)-entropies. We demonstr
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