Hutter, Marcus2015-08-312015-08-310022-0000http://hdl.handle.net/1885/15035This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff’s universal prior M, the latter being an excellent predictor in deterministic as well as probabilistic environments, where performance is measured in terms of convergence of posteriors or losses. Despite this closeness to M, it is difficult to assess the prediction quality of m, since little is known about the closeness of their posteriors, which are the important quantities for prediction. We show that for deterministic computable environments, the “posterior” and losses of m converge, but rapid convergence could only be shown on-sequence; the off-sequence convergence can be slow. In probabilistic environments, neither the posterior nor the losses converge, in general.© 2005 Elsevier Inc. http://www.sherpa.ac.uk/romeo/issn/0022-0000/..."Author's post-print on open access repository after an embargo period of between 12 months and 48 months" from SHERPA/RoMEO site (as at 31/08/15).http://creativecommons.org/licenses/ by-nc-nd/4.0/Sequential predictions based on algorithmic complexity2006-0210.1016/j.jcss.2005.07.001Creative Commons Attribution licence