Algorithmic complexity bounds on future prediction errors
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 are at a time t > 1 and have already observed x = x1 ⋯ xt. We bound the future prediction performance on x(t+1)x(t+2) ⋯ by a new variant of algorithmic complexity of μ given x, plus the complexity of the randomness deficiency of x. The new...[Show more]
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|Source:||Information and Computation|
|Chernov et al Algorithmic Complexity Bounds 2007.pdf||239.52 kB||Adobe PDF|
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