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：本文提出将概率视为一种相对度量而非绝对度量的观点。为阐释这一概念，我们聚焦于有限结果空间，建立了相对概率函数所需的三条基本公理。随后，我们提供了这些函数的示例库及其组合系统。此外，我们讨论了相对贝叶斯推断及其数字化实现。最后，我们证明了相对概率空间的拓扑封闭性，突显其在极限条件下保留信息的能力。