Parameter Learning Using Approximate Model Counting

Author:

Dierckx LucileORCID,Dubray AlexandreORCID,Nijssen SiegfriedORCID

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Chakraborty, S., Fremont, D., Meel, K., Seshia, S., Vardi, M.: Distribution-aware sampling and weighted model counting for SAT. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)

2. Chakraborty, S., Meel, K.S., Vardi, M.Y.: Algorithmic improvements in approximate counting for probabilistic inference: From linear to logarithmic SAT calls. Tech. rep. (2016)

3. Chavira, M., Darwiche, A.: On probabilistic inference by weighted model counting. Artifi. Intell. 172(6-7) (2008)

4. Darwiche, A.: A differential approach to inference in bayesian networks. J. ACM (JACM) 50(3), 280–305 (2003)

5. Darwiche, A.: SDD: A new canonical representation of propositional knowledge bases. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)

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