## Abstract

Markov chain Monte Carlo (MCMC) is a standard technique to sample from a target distribution by simulating Markov chains. In an analogous fashion to MCMC, this paper proposes a deterministic sampling algorithm based on deterministic random walk, such as the rotor-router model (a.k.a. Propp machine). For the algorithm, we give an upper bound of the point-wise distance (i.e., infinity norm) between the "distributions" of a deterministic random walk and its corresponding Markov chain in terms of the mixing time of the Markov chain. As a result, for uniform sampling of #P-complete problems, such as 0-1 knapsack solutions, linear extensions, matchings, etc., for which rapidly mixing chains are known, our deterministic algorithm provides samples with a "distribution" with a point-wise distance at most ε from the target distribution, in time polynomial in the input size and ε^{-1}.

Original language | English |
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Title of host publication | Computing and Combinatorics - 20th International Conference, COCOON 2014, Proceedings |

Publisher | Springer Verlag |

Pages | 25-36 |

Number of pages | 12 |

ISBN (Print) | 9783319087825 |

DOIs | |

Publication status | Published - 2014 |

Event | 20th International Computing and Combinatorics Conference, COCOON 2014 - Atlanta, GA, United States Duration: Aug 4 2014 → Aug 6 2014 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 8591 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 20th International Computing and Combinatorics Conference, COCOON 2014 |
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Country/Territory | United States |

City | Atlanta, GA |

Period | 8/4/14 → 8/6/14 |

## All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

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