Learning measurement models for unobserved variables

Authors:
Ricardo Silva;Richard Scheines;Clark Glymour;Peter Spirtes
Affiliations:
Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA;Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA;Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA;Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA
Venue:
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Year:
2002

Citing 2
Cited 1

Learning equivalence classes of bayesian-network structures

The Journal of Machine Learning Research
Asymptotic model selection for naive Bayesian networks

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence

New d-separation identification results for learning continuous latent variable models

ICML '05 Proceedings of the 22nd international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Observed associations in a database may be due in whole or part to variations in unrecorded ("latent") variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such that members of a class share only a single latent common cause, standard search algorithms for causal Bayes nets can infer structural relations between latent variables. We introduce an algorithm for discovering such partitions when they exist. Uniquely among available procedures, the algorithm is (asymptotically) correct under standard assumptions in causal Bayes net search algorithms, requires no prior knowledge of the number of latent variables, and does not depend on the mathematical form of the relationships among the latent variables. We evaluate the algorithm on a variety of simulated data sets.