Optimality of universal Bayesian sequence prediction for general loss and alphabet
Various optimality properties of universal sequence predictors based on Bayes-mixtures in general, and Solomonoff's prediction scheme in particular, will be studied. The probability of observing xt at time t, given past observations x1...xt-1 can be computed with the chain rule if the true generating distribution μ of the sequences x 1x2x3... is known. If μ is unknown, but known to belong to a countable or continuous class M one can base ones prediction on the Bayes-mixture ℰ defined as...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|01_Hutter_Optimality_of_universal_2003.pdf||325.55 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.