Non-impeding noisy-AND tree causal models over multi-valued variables

DOI10.1016/j.ijar.2012.03.002zbMath1264.68185OpenAlexW1976131145MaRDI QIDQ1951287

Yang Xiang

Publication date: 4 June 2013

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

Full work available at URL: https://doi.org/10.1016/j.ijar.2012.03.002

zbMATH Keywords

Bayesian networksknowledge acquisitionconditional probability distributionscausal probabilistic models

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (7)

Learning tractable NAT-modeled Bayesian networks ⋮ Efficient probabilistic inference in Bayesian networks with multi-valued NIN-AND tree local models ⋮ Propagation effects of model-calculated probability values in Bayesian networks ⋮ An intercausal cancellation model for Bayesian-network engineering ⋮ Direct causal structure extraction from pairwise interaction patterns in NAT modeling Bayesian networks ⋮ Compression of General Bayesian Net CPTs ⋮ The extended recursive noisy or model: static and dynamic considerations

Cites Work

• A generic qualitative characterization of independence of causal influence

This page was built for publication: Non-impeding noisy-AND tree causal models over multi-valued variables