Monotone conditional 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 we are at a time t>1 and already observed x=x 1...x t . 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 complexity is...[Show more]
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