, Volume 64, Issue 4, pp 606–620 | Cite as

Negative Examples for Sequential Importance Sampling of Binary Contingency Tables

  • Ivona Bezáková
  • Alistair Sinclair
  • Daniel Štefankovič
  • Eric Vigoda


The sequential importance sampling (SIS) algorithm has gained considerable popularity for its empirical success. One of its noted applications is to the binary contingency tables problem, an important problem in statistics, where the goal is to estimate the number of 0/1 matrices with prescribed row and column sums. We give a family of examples in which the SIS procedure, if run for any subexponential number of trials, will underestimate the number of tables by an exponential factor. This result holds for any of the usual design choices in the SIS algorithm, namely the ordering of the columns and rows. These are apparently the first theoretical results on the efficiency of the SIS algorithm for binary contingency tables. Finally, we present experimental evidence that the SIS algorithm is efficient for row and column sums that are regular. Our work is a first step in determining the class of inputs for which SIS is effective.


Sequential Monte Carlo Markov chain Monte Carlo Graphs with prescribed degree sequence Zero-one table 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Ivona Bezáková
    • 1
  • Alistair Sinclair
    • 2
  • Daniel Štefankovič
    • 3
  • Eric Vigoda
    • 4
  1. 1.Department of Computer ScienceRochester Institute of TechnologyRochesterUSA
  2. 2.Computer Science DivisionUniversity of CaliforniaBerkeleyUSA
  3. 3.Department of Computer ScienceUniversity of RochesterRochesterUSA
  4. 4.College of ComputingGeorgia Institute of TechnologyAtlantaUSA

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