2.50
Hdl Handle:
http://hdl.handle.net/10547/279181
Title:
On-line probability, complexity and randomness
Authors:
Chernov, Alexey; Shen, Alexander; Vereshchagin, Nikolai; Vovk, Vladimir
Abstract:
Classical probability theory considers probability distributions that assign probabilities to all events (at least in the finite case). However, there are natural situations where only part of the process is controlled by some probability distribution while for the other part we know only the set of possibilities without any probabilities assigned. We adapt the notions of algorithmic information theory (complexity, algorithmic randomness, martingales, a priori probability) to this framework and show that many classical results are still valid.
Citation:
Chernov, A., Shen, A., Vereshchagin, N. and Vovk , V., (2008) 'On-Line Probability, Complexity and Randomness' in Algorithmic Learning Theory, proceedings on the 19th International Conference, ALT 2008, vol. 5254: 138-153
Publisher:
Springer
Journal:
Algorithmic Learning Theory
Issue Date:
2008
URI:
http://hdl.handle.net/10547/279181
DOI:
10.1007/978-3-540-87987-9_15
Additional Links:
http://link.springer.com/chapter/10.1007/978-3-540-87987-9_15
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9783540879862
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorChernov, Alexeyen_GB
dc.contributor.authorShen, Alexanderen_GB
dc.contributor.authorVereshchagin, Nikolaien_GB
dc.contributor.authorVovk, Vladimiren_GB
dc.date.accessioned2013-04-07T16:51:53Z-
dc.date.available2013-04-07T16:51:53Z-
dc.date.issued2008-
dc.identifier.citationChernov, A., Shen, A., Vereshchagin, N. and Vovk , V., (2008) 'On-Line Probability, Complexity and Randomness' in Algorithmic Learning Theory, proceedings on the 19th International Conference, ALT 2008, vol. 5254: 138-153en_GB
dc.identifier.isbn9783540879862-
dc.identifier.doi10.1007/978-3-540-87987-9_15-
dc.identifier.urihttp://hdl.handle.net/10547/279181-
dc.description.abstractClassical probability theory considers probability distributions that assign probabilities to all events (at least in the finite case). However, there are natural situations where only part of the process is controlled by some probability distribution while for the other part we know only the set of possibilities without any probabilities assigned. We adapt the notions of algorithmic information theory (complexity, algorithmic randomness, martingales, a priori probability) to this framework and show that many classical results are still valid.en_GB
dc.language.isoenen
dc.publisherSpringeren_GB
dc.relation.urlhttp://link.springer.com/chapter/10.1007/978-3-540-87987-9_15en_GB
dc.titleOn-line probability, complexity and randomnessen
dc.typeConference papers, meetings and proceedingsen
dc.identifier.journalAlgorithmic Learning Theoryen_GB
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