On universal prediction and Bayesian confirmation
The Bayesian framework is a well-studied and successful framework for inductive reasoning, which includes hypothesis testing and confirmation, parameter estimation, sequence prediction, classification, and regression. But standard statistical guidelines for choosing the model class and prior are not always available or can fail, in particular in complex situations. Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and...[Show more]
|Collections||ANU Research Publications|
|Source:||Theoretical Computer Science|
|Hutter et al On Universal Prediction 2007.pdf||345.13 kB||Adobe PDF|
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