Seonglae ChoSolomonoff Induction considers all hypotheses as candidates, assigning higher weights to simpler hypotheses based on their Kolmogorov Complexity, providing a mathematical justification for Occam's Razor. For observed data, it evaluates the probability of all possible programs that could generate that data, with simpler programs being considered more likely. This implements an ideal Bayesian approach to predicting future data.
While theoretically powerful, it is computationally impossible to implement in practice since it requires considering all possible programs. Instead, this model serves as a theoretical limit and ideal standard for inference.
