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Biased Boltzmann samplers and generation of extended linear languages with shuffle

Abstract : 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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https://hal.inria.fr/hal-01197245
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  • HAL Id : hal-01197245, version 1

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Alexis Darrasse, Konstantinos Panagiotou, Olivier Roussel, Michele Soria. Biased Boltzmann samplers and generation of extended linear languages with shuffle. 23rd International Meeting on Probabilistic, Combinatorial, and Asymptotic Methods in the Analysis of Algorithms (AofA'12), Jun 2012, Montreal, Canada. pp.125-140. ⟨hal-01197245⟩

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