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Monotone conditional complexity bounds on future prediction errors

Date

Authors

Chernov, Alexey

Hutter, Marcus

Publisher

Springer

Abstract

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 distribution μ by the algorithmic complexity of μ. Here we assume that we ar

Keywords

Keywords: Kolmogorov complexity; Monotone conditional complexity; Online sequential prediction; Posterior bounds; Randomness deficiency; Solomonoff prior; Algorithms; Bayesian networks; Computational complexity; Forecasting; Random processes; Parallel processing sy Future loss; Kolmogorov complexity; Monotone conditional complexity; Online sequential prediction; Posterior bounds; Randomness deficiency; Solomonoff prior; Total error

URI

http://hdl.handle.net/1885/57726

Collections

ANU Research Publications

Source

Algorithmic Learning Theory: Proceedings of the 16th International Conference on Algorithmic Learning Theory (ALT-05) - LNAI 3734

Type

Conference paper

DOI

10.1016/j.ic.2006.10.004

Restricted until

2037-12-31

Downloads

File

Description

01_Chernov_Monotone_conditional_2005.pdf (255.49 KB)

02_Chernov_Monotone_conditional_2005.pdf (95.28 KB)

03_Chernov_Monotone_conditional_2005.pdf (94.7 KB)