DMTCS Proceedings, 23rd Intern. Meeting on Probabilistic, Combinatorial, and Asymptotic Methods for the Analysis of Algorithms (AofA'12)

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Biased Boltzmann samplers and generation of extended linear languages with shuffle

Alexis Darrasse, Konstantinos Panagiotou, Olivier Roussel, Michèle Soria


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.

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