This work proposes a view of probability as a relative measure rather than an absolute one. To demonstrate this concept, we focus on finite outcome spaces and develop three fundamental axioms that establish requirements for relative probability functions. We then provide a library of examples of these functions and a system for composing them. Additionally, we discuss a relative version of Bayesian inference and its digital implementation. Finally, we prove the topological closure of the relative probability space, highlighting its ability to preserve information under limits.

翻译：本文将概率视为一种相对度量而非绝对度量。为阐明该概念，我们聚焦有限结果空间，建立三项基本公理以确立相对概率函数的必要条件。随后提供此类函数的示例库及其复合系统。此外，我们探讨贝叶斯推断的相对版本及其数字实现。最后，证明相对概率空间的拓扑闭包性质，凸显其在极限条件下保持信息的能力。