{"paper":{"title":"Characterizing predictable classes of processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.PR"],"primary_cat":"cs.AI","authors_text":"Daniil Ryabko (INRIA Futurs, INRIA Lille - Nord Europe), LIFL","submitted_at":"2009-05-27T06:31:32Z","abstract_excerpt":"The problem is sequence prediction in the following setting. A sequence $x_1,...,x_n,...$ of discrete-valued observations is generated according to some unknown probabilistic law (measure) $\\mu$. After observing each outcome, it is required to give the conditional probabilities of the next observation. The measure $\\mu$ belongs to an arbitrary class $\\C$ of stochastic processes. We are interested in predictors $\\rho$ whose conditional probabilities converge to the \"true\" $\\mu$-conditional probabilities if any $\\mu\\in\\C$ is chosen to generate the data. We show that if such a predictor exists, t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0905.4341","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}