  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.
