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Conference papers

Bayesian networks : a better than frequentist approach for parametrization, and a more accurate structural complexity measure than the number of parameters

Sylvain Gelly 1 Olivier Teytaud 1
1 TANC - Algorithmic number theory for cryptology
Inria Saclay - Ile de France, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
Abstract : We propose and justify a better-than-frequentist approach for bayesian network parametrization, and propose a structural entropy term that more precisely quantifies the complexity of a BN than the number of parameters. Algorithms for BN learning are deduced.
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Conference papers
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https://hal.inria.fr/inria-00000541
Contributor : Olivier Teytaud Connect in order to contact the contributor
Submitted on : Monday, October 31, 2005 - 10:34:05 PM
Last modification on : Friday, February 4, 2022 - 3:16:52 AM
Long-term archiving on: : Friday, April 2, 2010 - 6:14:18 PM

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Sylvain Gelly, Olivier Teytaud. Bayesian networks : a better than frequentist approach for parametrization, and a more accurate structural complexity measure than the number of parameters. CAP, 2005, Nice, 16 p. ⟨inria-00000541⟩

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