Solomonoff Induction is a theoretical framework for predicting the continuation of any sequence of data, based on the principle that among all possible explanations, the simplest one that fits the data is most likely correct. It combines ideas from algorithmic information theory and Bayesian probability to provide a universal method for prediction and learning.