TY - GEN

T1 - Analytic samplers and the combinatorial rejection method

AU - Bodini, Olivier

AU - Lumbroso, Jeremie

AU - Rolin, Nicolas

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Boltzmann samplers, introduced by Duchon et al. in 2001, make it possible to uniformly draw approximate size objects from any class which can be specified through the symbolic method. This, through by evaluating the associated generating functions to obtain the correct branching probabilities. But these samplers require generating functions, in particular in the neighborhood of their sunglarity, which is a complex problem; they also require picking an appropriate tuning value to best control the size of generated objects. Although Pivoteau et al. have brought a sweeping question to the first question, with the introduction of their Newton oracle, questions remain. By adapting the rejection method, a classical tool from the random, we show how to obtain a variant of the Boltzmann sampler framework, which is tolerant of approximation, even large ones. Our goal for this is twofold: this allows for exact sampling with approximate values; but this also allows much more flexibility in tuning samplers. For the class of simple trees, we will show how this could be used to more easily calibrate samplers.

AB - Boltzmann samplers, introduced by Duchon et al. in 2001, make it possible to uniformly draw approximate size objects from any class which can be specified through the symbolic method. This, through by evaluating the associated generating functions to obtain the correct branching probabilities. But these samplers require generating functions, in particular in the neighborhood of their sunglarity, which is a complex problem; they also require picking an appropriate tuning value to best control the size of generated objects. Although Pivoteau et al. have brought a sweeping question to the first question, with the introduction of their Newton oracle, questions remain. By adapting the rejection method, a classical tool from the random, we show how to obtain a variant of the Boltzmann sampler framework, which is tolerant of approximation, even large ones. Our goal for this is twofold: this allows for exact sampling with approximate values; but this also allows much more flexibility in tuning samplers. For the class of simple trees, we will show how this could be used to more easily calibrate samplers.

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M3 - Conference contribution

AN - SCOPUS:84959133076

T3 - 12th Workshop on Analytic Algorithmics and Combinatorics 2015, ANALCO 2015

SP - 40

EP - 50

BT - 12th Workshop on Analytic Algorithmics and Combinatorics 2015, ANALCO 2015

PB - Society for Industrial and Applied Mathematics Publications

T2 - 12th Workshop on Analytic Algorithmics and Combinatorics, ANALCO 2015

Y2 - 4 January 2015

ER -