Solomonoff Induction

Solomonoff Induction is a theoretical method for making predictions about future data based on all possible ways data could be generated. It considers every possible computer program that could produce the observed data, weighting shorter (simpler) programs more heavily because they are more likely to be correct. By combining these weighted predictions, it aims to find the most probable explanation for what comes next. While it provides a rigorous foundation for understanding inductive reasoning, it is computationally impractical, serving more as an ideal standard for understanding intelligent prediction.