In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior[clarification needed] obtained by this formula[which?], in Bayes' rule for prediction [example needed][further explanation needed].
In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated.