Learning Bayesian networks with incomplete data by augmentation

Tameem Adel, Cassio P. De Campos

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)


We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks with such new approach.

Originele taal-2Engels
TitelProceedings of the Thirty-First AAAI Conference on Artificial Intelligence
UitgeverijAssociation for the Advancement of Artificial Intelligence (AAAI)
Aantal pagina's7
StatusGepubliceerd - dec 2016
Extern gepubliceerdJa
Evenement31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, Verenigde Staten van Amerika
Duur: 4 feb 201710 feb 2017

Publicatie series

NaamAAAI Conference on Artificial Intelligence


Congres31st AAAI Conference on Artificial Intelligence, AAAI 2017
LandVerenigde Staten van Amerika
StadSan Francisco

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