d-Separation: From Theorems to Algorithms
arXiv:1304.1505
Abstract
An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.
Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989)
Cited by in corpus (7)
- Graphical Normalizing Flows
- Causal inference for process understanding in Earth sciences
- Data Obsolescence Detection in the Light of Newly Acquired Valid Observations
- Probabilistic Warnings in National Security Crises: Pearl Harbor Revisited
- Solution Enumeration by Optimality in Answer Set Programming
- Sum-Product Network Decompilation
- Query the model: precomputations for efficient inference with Bayesian Networks