Latent variable models and factors analysis
Latent variable models and factors analysis
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
"Ideal Parent" structure learning for continuous variable networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On the testable implications of causal models with hidden variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning measurement models for unobserved variables
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Bayesian learning of measurement and structural models
ICML '06 Proceedings of the 23rd international conference on Machine learning
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Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the d-separations that define the graphical structure. This paper describes new distribution-free techniques for identifying d-separations in continuous latent variable models when non-linear dependencies are allowed among hidden variables.