Learning Bayesian Networks That Enable Full Propagation of Evidence

Learning Bayesian Networks That Enable Full Propagation of Evidence

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent....

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Bibliographic Details
Main Author: Anthony C. Constantinou
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Causal discovery
conditional independence
directed acyclic graphs
probabilistic graphical models
structure learning
Online Access:https://ieeexplore.ieee.org/document/9136714/

Internet

https://ieeexplore.ieee.org/document/9136714/

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