12 search results - page 2 / 3 » Convergence and Error Bounds for Universal Prediction of Non... |
CORR
2006
Springer
13 years 5 months ago
2006
Springer
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...
DAGSTUHL
2006
13 years 6 months ago
2006
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true dis...
CORR
2007
Springer
13 years 5 months ago
2007
Springer
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true d...
CORR
2006
Springer
13 years 5 months ago
2006
Springer
The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total...
CJ
2008
13 years 5 months ago
2008
We will describe three kinds of probabilistic induction problems, and give general solutions for each , with associated convergence theorems that show they tend to give good proba...