On the Use of Causal Graphical Models for Designing Experiments in the Automotive Domain.
David Issa MattosYuchu LiuPublished in: EASE (2022)
Keyphrases
- graphical models
- bayesian networks
- probabilistic model
- belief propagation
- probabilistic graphical models
- probabilistic inference
- random variables
- approximate inference
- conditional random fields
- structure learning
- possibilistic networks
- markov networks
- map inference
- conditional independence
- gaussian graphical models
- exact inference
- graph structure
- markov random field
- influence diagrams
- factor graphs
- structural learning
- models with hidden variables
- statistical inference
- loopy belief propagation
- causal networks
- chain graphs
- directed acyclic