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Article . 2022
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https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY
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Article . 2022
Data sources: DBLP
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Article . 2021
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The Intersection of Algorithmically Random Closed Sets and Effective Dimension

The intersection of algorithmically random closed sets and effective dimension
Authors: Adam Case; Christopher P. Porter;

The Intersection of Algorithmically Random Closed Sets and Effective Dimension

Abstract

In this article, we study several aspects of the intersections of algorithmically random closed sets. First, we answer a question of Cenzer and Weber, showing that the operation of intersecting relatively random closed sets (random with respect to certain underlying measures induced by Bernoulli measures on the space of codes of closed sets), which preserves randomness, can be inverted: a random closed set of the appropriate type can be obtained as the intersection of two relatively random closed sets. We then extend the Cenzer/Weber analysis to the intersection of multiple random closed sets, identifying the Bernoulli measures with respect to which the intersection of relatively random closed sets can be non-empty. We lastly apply our analysis to provide a characterization of the effective Hausdorff dimension of sequences in terms of the degree of intersectability of random closed sets that contain them.

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Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, computability theory, Mathematics - Logic, Galton-Watson processes, Algorithmic information theory (Kolmogorov complexity, etc.), Logic in Computer Science (cs.LO), algorithmic randomness, effective dimension, Algorithmic randomness and dimension, Branching processes (Galton-Watson, birth-and-death, etc.), FOS: Mathematics, random closed sets, Logic (math.LO)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green