Local conditioning in Bayesian networks

DOI10.1016/0004-3702(95)00118-2zbMath1506.68159OpenAlexW2072340691MaRDI QIDQ2676575

Francisco Javier Díez

Publication date: 27 September 2022

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0004-3702(95)00118-2

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37) Probabilistic graphical models (62H22)

Related Items (7)

Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering ⋮ Topological parameters for time-space tradeoff ⋮ Recursive conditioning ⋮ Anytime anyspace probabilistic inference ⋮ Evaluation of Bayesian networks with flexible state-space abstraction methods ⋮ A justification of local conditioning in Bayesian networks ⋮ Complexity of probabilistic reasoning in directed-path singly-connected Bayes networks

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Fusion, propagation, and structuring in belief networks
• Approximating probabilistic inference in Bayesian belief networks is NP- hard
• Probabilistic inference in multiply connected belief networks using loop cutsets
• Fusion and propagation with multiple observations in belief networks
• The computational complexity of probabilistic inference using Bayesian belief networks
• DISTRIBUTED INFERENCE IN BAYESIAN NETWORKS
• An algebra of bayesian belief universes for knowledge‐based systems

This page was built for publication: Local conditioning in Bayesian networks