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一个概率的过度总和规则:解决递归 ("和蛋") 问题

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我们介绍了超级总和规则 (HSR) 用于计算依赖事件的概率,证明它.

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科学领域:

  • 概率理论的概率理论是什么
  • 信息理论是信息理论.
  • 统计力学就是统计力学.

背景情况:

  • 常规总和规则 (CSR) 排除了递归概率,限制了其适用性.
  • 事件中的递归依赖性在AI和机器学习中很常见.
  • 当前处理复杂概率计算的方法往往难以解决.

研究的目的:

  • 介绍和验证概率的过度总和规则 (HSR).
  • 证明HSR的最大 (MaxEnt) 属性.
  • 确定概率的物理性质及其与热力学的联系.

主要方法:

  • HSR的导数,显示其对过度触角双角公式的同型性.
  • 证明HSR最大 (MaxEnt) 属性的证明.
  • 递归依赖关系的分析及其对概率计算的影响.

主要成果:

  • HSR准确计算了递归依赖事件的概率.
  • 已经证明HSR是最大值 (MaxEnt).
  • 与CSR不同,HSR可以实现可扩展和分析计算.

结论:

  • 由于其更广泛的适用性,HSR应该是概率计算的默认方法.
  • 概率是一个物理量,而不仅仅是一个数学构造.
  • 高压反应对数字信号处理和定量几何热力学有影响.