Solomonoff Induction
Theory of prediction that combines elements of algorithmic information theory and Bayesian inference to create a universal framework for inferring future data from past observations.
Solomonoff Induction is rooted in the concept of algorithmic probability, which suggests that the best hypothesis for a given set of observations is the one that can be most succinctly described by the shortest computer program producing them. This approach is inherently Bayesian as it involves updating probabilities based on new evidence. However, unlike traditional Bayesian inference, which requires a predefined hypothesis space and prior probabilities, Solomonoff Induction generates these automatically by considering all possible computable hypotheses. This method is considered semi-computable, as it theoretically provides a mechanism to predict future events given past data, but in practice, calculating the exact predictions is unfeasible due to its computational complexity.
The concept of Solomonoff Induction was introduced by Ray Solomonoff in the 1960s as part of his work on algorithmic information theory. It was a pioneering effort to formally address the problem of induction, aiming to predict future data based on past observations in a way that minimizes the prior assumptions.
Ray Solomonoff is the foundational figure behind this concept. His work laid the groundwork for later developments in algorithmic information theory and machine learning, influencing areas like computational complexity theory and the philosophical foundations of probability and prediction.