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@inproceedings{CMMV15,
  title={Approximate Probabilistic Inference via Word-Level Counting},
  bib2html_dl_pdf={../Papers/AAAI16.pdf},
  bib2html_rescat={Counting},
  code={https://bitbucket.org/kuldeepmeel/smtapproxmc},
  author={
    Chakraborty, Supratik and Meel, Kuldeep S. and Mistry, Rakesh and Vardi,
    Moshe Y.
  },
  year={2016},
  month=jun,
  booktitle=AAAI,
  bib2html_pubtype={Refereed Conference},
  abstract={
    Hashing-based model counting has emerged as a promising
    approach for large-scale probabilistic inference on graphical
    models. A key component of these techniques is the
    use of xor-based 2-universal hash functions that operate over
    Boolean domains. Many counting problems arising in probabilistic
    inference are, however, naturally encoded over fi-
    nite discrete domains. Techniques based on bit-level (or
    Boolean) hash functions require these problems to be propositionalized,
    making it impossible to leverage the remarkable
    progress made in SMT (Satisfiability Modulo Theory)
    solvers that can reason directly over words (or bit-vectors). In
    this work, we present the first approximate model counter that
    uses word-level hashing functions, and can directly leverage
    the power of sophisticated SMT solvers. Empirical evaluation
    over an extensive suite of benchmarks demonstrates the
    promise of the approach.
  },
}