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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{APM21,
  title={Partition Function Estimation: A Quantitative Study},
  author={Agrawal, Durgesh and Pote, Yash and Meel, Kuldeep S.},
  booktitle=IJCAI,
  year={2021},
  month=aug,
  bib2html_rescat={Counting},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/abs/2105.11132},
  abstract={
    Probabilistic graphical models have emerged as a powerful modeling tool for
    several real-world scenarios where one needs to reason under uncertainty. A
    graphical model's partition function is a central quantity of interest, and
    its computation is key to several probabilistic reasoning tasks. Given the
    #P-hardness of computing the partition function, several techniques have
    been proposed over the years with varying guarantees on the quality of
    estimates and their runtime behavior. This paper seeks to present a survey
    of 17 techniques and a rigorous empirical study of their behavior across an
    extensive set of benchmarks. Our empirical study draws up a surprising
    observation: exact techniques are as efficient as the approximate ones, and
    therefore, we conclude with an optimistic view of opportunities for the
    design of approximate techniques with enhanced scalability. Motivated by the
    observation of an order of magnitude difference between the Virtual Best
    Solver and the best performing tool, we envision an exciting line of
    research focused on the development of portfolio solvers.
  },
}