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Conference Papers Year : 2023

Uniform SAmplINg with BOLTZmann

Matthieu Dien
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Martin Pépin

Abstract

USAIN BOLTZ is a fast Python library for the uniform random generation of tree-like structures. It allows the user to specify both (1) the data structure they wish to sample, using simple combinators similar to those of context-free grammars, and (2) their memory representation. The underlying algorithms are optimised Boltzmann samplers allowing to get approximatesize uniform random generation in linear time. Experimental results show that USAIN BOLTZ matches the performance of the experimental Arbogen package for OCaml, and out-performs the Boltzmann brain Haskell library, while being easier to integrate into existing scientific tools such as Sagemath.
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Dates and versions

hal-04391195 , version 1 (12-01-2024)

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Matthieu Dien, Martin Pépin. Uniform SAmplINg with BOLTZmann. 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, Sep 2023, Nancy, France. pp.94-97, ⟨10.1109/SYNASC61333.2023.00020⟩. ⟨hal-04391195⟩
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