Algorithmic Probability

Algorithmic probability is a mathematical method involving assigning a prior probability into a given observation. Algorithmic probability combines Occam’s razor and also the principle of multiple explanations by providing a probability worth to each theory that explains certain observation, with the simplest hypothesis having the biggest probability and the particular increasingly complex hypotheses receiving increasingly little probabilities.