Algorithmic Randomness and Capacity of Closed Sets

We investigate the connection between measure, capacity and algorithmic randomness for the space of closed sets. For any computable measure m, a computable capacity T may be defined by letting T(Q) be the measure of the family of closed sets K which have nonempty intersection with Q. We prove an eff...

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Main Authors: Douglas Cenzer, Paul Brodhead, Ferit Toska, Sebastian Wyman
Format: Article
Language:English
Published: Logical Methods in Computer Science e.V. 2011-09-01
Series:Logical Methods in Computer Science
Subjects:
Online Access:https://lmcs.episciences.org/1020/pdf
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author Douglas Cenzer
Paul Brodhead
Ferit Toska
Sebastian Wyman
author_facet Douglas Cenzer
Paul Brodhead
Ferit Toska
Sebastian Wyman
author_sort Douglas Cenzer
collection DOAJ
description We investigate the connection between measure, capacity and algorithmic randomness for the space of closed sets. For any computable measure m, a computable capacity T may be defined by letting T(Q) be the measure of the family of closed sets K which have nonempty intersection with Q. We prove an effective version of Choquet's capacity theorem by showing that every computable capacity may be obtained from a computable measure in this way. We establish conditions on the measure m that characterize when the capacity of an m-random closed set equals zero. This includes new results in classical probability theory as well as results for algorithmic randomness. For certain computable measures, we construct effectively closed sets with positive capacity and with Lebesgue measure zero. We show that for computable measures, a real q is upper semi-computable if and only if there is an effectively closed set with capacity q.
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spelling doaj.art-9f67e37928e34cbe89677a5f6081b8ec2024-03-08T09:17:23ZengLogical Methods in Computer Science e.V.Logical Methods in Computer Science1860-59742011-09-01Volume 7, Issue 310.2168/LMCS-7(3:16)20111020Algorithmic Randomness and Capacity of Closed SetsDouglas CenzerPaul BrodheadFerit ToskaSebastian WymanWe investigate the connection between measure, capacity and algorithmic randomness for the space of closed sets. For any computable measure m, a computable capacity T may be defined by letting T(Q) be the measure of the family of closed sets K which have nonempty intersection with Q. We prove an effective version of Choquet's capacity theorem by showing that every computable capacity may be obtained from a computable measure in this way. We establish conditions on the measure m that characterize when the capacity of an m-random closed set equals zero. This includes new results in classical probability theory as well as results for algorithmic randomness. For certain computable measures, we construct effectively closed sets with positive capacity and with Lebesgue measure zero. We show that for computable measures, a real q is upper semi-computable if and only if there is an effectively closed set with capacity q.https://lmcs.episciences.org/1020/pdfcomputer science - logic in computer sciencemathematics - logicf.4.1
spellingShingle Douglas Cenzer
Paul Brodhead
Ferit Toska
Sebastian Wyman
Algorithmic Randomness and Capacity of Closed Sets
Logical Methods in Computer Science
computer science - logic in computer science
mathematics - logic
f.4.1
title Algorithmic Randomness and Capacity of Closed Sets
title_full Algorithmic Randomness and Capacity of Closed Sets
title_fullStr Algorithmic Randomness and Capacity of Closed Sets
title_full_unstemmed Algorithmic Randomness and Capacity of Closed Sets
title_short Algorithmic Randomness and Capacity of Closed Sets
title_sort algorithmic randomness and capacity of closed sets
topic computer science - logic in computer science
mathematics - logic
f.4.1
url https://lmcs.episciences.org/1020/pdf
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AT paulbrodhead algorithmicrandomnessandcapacityofclosedsets
AT ferittoska algorithmicrandomnessandcapacityofclosedsets
AT sebastianwyman algorithmicrandomnessandcapacityofclosedsets