文献类型：期刊文献

英文题名：Information entropy of complex probability

作者：Li, Chan[1]; Xu, Hejun[2]; Cao, Zhu[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Fengqun Network Technology Co., Ltd., Shanghai, 200237, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250124239)

语种：英文

外文关键词：Entropy

摘要：Probability theory is fundamental for modeling uncertainty, with traditional probabilities being real and non-negative. Complex probability extends this concept by allowing complex-valued probabilities, opening new avenues for analysis in various fields. This paper explores the information-theoretic aspects of complex probability, focusing on its definition, properties, and applications. We extend Shannon entropy to complex probability and examine key properties, including maximum entropy, joint entropy, conditional entropy, equilibration, and cross entropy. These results offer a framework for understanding entropy in complex probability spaces and have potential applications in fields such as statistical mechanics and information theory. Copyright ? 2025, The Authors. All rights reserved.