Or you may switch between versions of your files for experimental features.
The figures denote the cells of the table involved in each metric, the probability being the fraction of each figure that is shaded. P(A|B) = Bayesian inference derives the posterior probability as a consequence of two antecedents, a prior probability and a "likelihood function" derived from a statistical model for the observed data.
A version control system (VCS) allows you to track the history of a collection of files.
It supports creating different versions of this collection.
For example, if one does not know whether the newborn baby next door is a boy or a girl, the color of decorations on the crib in front of the door may support the hypothesis of one gender or the other; but if in front of that door, instead of the crib, a dog kennel is found, the posterior probability that the family next door gave birth to a dog remains small in spite of the "evidence", since one's prior belief in such a hypothesis was already extremely small.
The critical point about Bayesian inference, then, is that it provides a principled way of combining new evidence with prior beliefs, through the application of Bayes' rule.