Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation

Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt

AAAI 2019 pp. 7825-7833

doi:10.1609/AAAI.V33I01.33017825 /aaai/2019/martires2019aaai-exact/

Abstract

Weighted model counting has recently been extended to weighted model integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted model integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.

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Cite

Text

Dos Martires et al. "Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33017825

Markdown

[Dos Martires et al. "Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/martires2019aaai-exact/) doi:10.1609/AAAI.V33I01.33017825

BibTeX

@inproceedings{martires2019aaai-exact,
  title     = {{Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation}},
  author    = {Dos Martires, Pedro Zuidberg and Dries, Anton and De Raedt, Luc},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {7825-7833},
  doi       = {10.1609/AAAI.V33I01.33017825},
  url       = {https://mlanthology.org/aaai/2019/martires2019aaai-exact/}
}