Algorithmic probability, also known as Solomonoff probability, is a method in algorithmic information theory for assigning prior probabilities to observations based on the programs that could generate them. It links probability, computation, and description length, treating simpler generative explanations as more probable.
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.