Inverse probability

Today, the problem of determining an unobserved variable (by whatever method) is called inferential statistics.

[1] Fisher uses the term in Fisher (1922), referring to "the fundamental paradox of inverse probability" as the source of the confusion between statistical terms that refer to the true value to be estimated, with the actual value arrived at by the estimation method, which is subject to error.

The term "Bayesian", which displaced "inverse probability", was introduced by Ronald Fisher in 1950.

[2] Inverse probability, variously interpreted, was the dominant approach to statistics until the development of frequentism in the early 20th century by Ronald Fisher, Jerzy Neyman and Egon Pearson.

A simple example would be the problem of estimating the position of a star in the sky (at a certain time on a certain date) for purposes of navigation.