Learning parameters of Bayesian networks from incomplete data via importance sampling

DOI10.1016/j.ijar.2005.10.005zbMath1096.68706OpenAlexW2092641131MaRDI QIDQ2499050

Carsten Riggelsen

Publication date: 14 August 2006

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2005.10.005

zbMATH Keywords

Bayesian networksincomplete dataBayesian statisticsMCMCparameter learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Performance evaluation of imputation based on Bayesian networks ⋮ Bayesian network models for incomplete and dynamic data ⋮ Inferring gene regulatory networks by an order independent algorithm using incomplete data sets ⋮ A new approach for probability calculation of fuzzy events in Bayesian networks

Uses Software

• WinBUGS

Cites Work

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• The EM algorithm for graphical association models with missing data
• A Bayesian method for the induction of probabilistic networks from data
• Learning Bayesian networks: The combination of knowledge and statistical data
• Sequential updating of conditional probabilities on directed graphical structures
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A fast procedure for model search in multidimensional contingency tables
• The Calculation of Posterior Distributions by Data Augmentation
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Weighted Average Importance Sampling and Defensive Mixture Distributions

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