A perfect sampling algorithm of random walks with forbidden arcs

Stéphane Durand 1 Bruno Gaujal 1 Florence Perronnin 1 Jean-Marc Vincent 1
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this paper we show how to construct an algorithm to sample the stationary distribution of a random walk over ${1,...,N}^d$ with forbidden arcs. This algorithm combines the rejection method and coupling from the past of a set of trajectories of the Markov chain that generalizes the classical sandwich approach. We also provide a complexity analysis of this approach in several cases showing a coupling time in $O(N^2 d log d )$ when no arc is forbidden and an experimental study of its performance.
Type de document :
Communication dans un congrès
QEST 2014 - 11th International Conference on Quantitative Evaluation of Systems, Sep 2014, Florence, Italy. Springer, 8657, pp.178-193, 2014, LNCS; Quantitative Evaluation of Systems. <10.1007/978-3-319-10696-0_15>
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https://hal.inria.fr/hal-01069975
Contributeur : Florence Perronnin <>
Soumis le : mardi 30 septembre 2014 - 11:43:53
Dernière modification le : mardi 22 mars 2016 - 01:27:18

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Stéphane Durand, Bruno Gaujal, Florence Perronnin, Jean-Marc Vincent. A perfect sampling algorithm of random walks with forbidden arcs. QEST 2014 - 11th International Conference on Quantitative Evaluation of Systems, Sep 2014, Florence, Italy. Springer, 8657, pp.178-193, 2014, LNCS; Quantitative Evaluation of Systems. <10.1007/978-3-319-10696-0_15>. <hal-01069975>

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