Biased Boltzmann samplers and generation of extended linear languages with shuffle

This paper is devoted to the construction of Boltzmann samplers according to various distributions, and uses stochastic bias on the parameter of a Boltzmann sampler, to produce a sampler with a different distribution for the size of the output. As a significant application, we produce Boltzmann samp...

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Vydáno v:Discrete mathematics and theoretical computer science Ročník DMTCS Proceedings vol. AQ,...; číslo Proceedings; s. 125 - 140
Hlavní autoři: Darrasse, Alexis, Panagiotou, Konstantinos, Roussel, Olivier, Soria, Michele
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: DMTCS 01.01.2012
Discrete Mathematics and Theoretical Computer Science
Discrete Mathematics & Theoretical Computer Science
Edice:DMTCS Proceedings
Témata:
[info.info-cg] computer science [cs]/computational geometry [cs.cg]
[info.info-dm] computer science [cs]/discrete mathematics [cs.dm]
[info.info-ds] computer science [cs]/data structures and algorithms [cs.ds]
[math.math-co] mathematics [math]/combinatorics [math.co]
biased distributions
boltzmann samplers
combinatorial specifications
Combinatorics
Computational Geometry
Computer Science
Data Structures and Algorithms
Discrete Mathematics
generating functions
Mathematics
random sampling
shuffle operator
generating functions
random sampling
combinatorial specifications
shuffle operator
Boltzmann samplers
biased distributions
ISSN:1365-8050, 1462-7264, 1365-8050
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Popis
Shrnutí:This paper is devoted to the construction of Boltzmann samplers according to various distributions, and uses stochastic bias on the parameter of a Boltzmann sampler, to produce a sampler with a different distribution for the size of the output. As a significant application, we produce Boltzmann samplers for words defined by regular specifications containing shuffle operators and linear recursions. This sampler has linear complexity in the size of the output, where the complexity is measured in terms of real-arithmetic operations and evaluations of generating functions.
ISSN:1365-8050
1462-7264
1365-8050
DOI:10.46298/dmtcs.2989