On the Foundations of Universal Sequence Prediction [chapter]

Marcus Hutter
2006 Lecture Notes in Computer Science  
Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction. We show that Solomonoff's model possesses many desirable properties: Fast convergence and strong bounds, and in contrast to most classical continuous prior densities has no zero
more » ... ior problem, i.e. can confirm universal hypotheses, is reparametrization and regrouping invariant, and avoids the old-evidence and updating problem. It even performs well (actually better) in non-computable environments.
doi:10.1007/11750321_39 fatcat:2u7ervgkjnhb3mrmfsbbsqmcuy