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Published October 25, 2021 | Version nips21
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aditya95sriram/bn-slim: NeurIPS 2021

  • 1. Technische Universität Wien

Description

Version submitted to NeurIPS 2021 as a part of the paper titled Learning Fast-Inference Bayesian Networks. Implements bounded state space size Bayesian Network learning.

Notes

Also funded in part by WWTF (project ICT19-065)

Files

aditya95sriram/bn-slim-nips21.zip

Files (7.7 MB)

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Additional details

Related works

Funding

FWF Austrian Science Fund
SAT-Based Local Improvement Methods (SLIM) P 32441
FWF Austrian Science Fund
Vollantrag zu Logical Methods in Computer Science W 1255

References

  • Benjumeda, Marco and Bielza, Concha and Larrañaga Pedro. Learning tractable Bayesian Networks in the space of elimination orders. Artificial Intelligence, 274:66–90, 2019
  • Scanagatta, Mauro. BLIP – Bayesian network learning and inference package, 2015. URL: https: //ipg.idsia.ch/software/blip