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：这项工作建议将概率视为一个相对的尺度,而不是一个绝对的尺度。为了证明这一概念,我们注重有限的结果空间,并开发三个基本轴,确定相对概率功能的要求。然后我们提供这些功能的示例库和形成这些功能的系统。此外,我们讨论贝叶斯推论的相对版本及其数字应用。最后,我们证明相对概率空间的表面封闭,突出其保存限制信息的能力。