In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. In a theoretic sense, the prior is universal. It is used in inductive inference theory, and analyses of algorithms. Since it is not computable, it must be approximated.