# What is Solomonoff Induction?

Solomonoff induction is a technique used for making predictions based on the idea that the best predictor of future events is the simplest prediction that also fits past data.

This system, in a certain sense, is the perfect universal prediction algorithm because it will learn to correctly predict any computable sequence with only the absolute minimum amount of data.

At its core, it is based on the idea of Bayesian probability theory and Occam's Razor, which suggests that the simplest explanation for a particular phenomenon is most likely to be correct.

Given whatever data points that we have, the algorithm analyses and assigns probabilities to each program that could have resulted in this data being generated. It then uses Occam's Razor to assign higher probabilities to programs that are simpler and shorter in length. The final output of the algorithm is a probability distribution over all possible programs.

In this way, Solomonoff's induction attempts to combine Occam's Razor, which favors simpler explanations, with Bayesian reasoning, which updates beliefs based on new data.