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Partial homoscedasticity in causal discovery with linear models
J Wu, M Drton - IEEE Journal on Selected Areas in Information …, 2023 - ieeexplore.ieee.org
Recursive linear structural equation models and the associated directed acyclic graphs
(DAGs) play an important role in causal discovery. The classic identifiability result for this …
(DAGs) play an important role in causal discovery. The classic identifiability result for this …
Block Domain Knowledge-Driven Learning of Chain Graphs Structure
S Yang, F Cao - Journal of Artificial Intelligence Research, 2024 - jair.org
As the interdependence between arbitrary objects in the real world grows, it becomes
gradually important to use chain graphs containing directed and undirected edges to learn …
gradually important to use chain graphs containing directed and undirected edges to learn …
Chain graph structure learning based on minimal c-separation trees
L Tan, Y Sun, Y Du - International Journal of Approximate Reasoning, 2024 - Elsevier
Chain graphs are a comprehensive class of graphical models that describe conditional
independence information, encompassing both Markov networks and Bayesian networks as …
independence information, encompassing both Markov networks and Bayesian networks as …
Identifiability and Consistent Estimation for Gaussian Chain Graph Models
The chain graph model admits both undirected and directed edges in one graph, where
symmetric conditional dependencies are encoded via undirected edges and asymmetric …
symmetric conditional dependencies are encoded via undirected edges and asymmetric …