Heuristic Greedy Search Algorithms for Latent Variable Models (1997)
by
Peter Spirtes
,
Thomas Richardson
,
Chris Meek
| Citations: | 9 - 1 self |
BibTeX
@MISC{Spirtes97heuristicgreedy,
author = {Peter Spirtes and Thomas Richardson and Chris Meek},
title = {Heuristic Greedy Search Algorithms for Latent Variable Models},
year = {1997}
}
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Abstract
this paper we will describe how to extend search algorithms developed for non-latent variable DAG models to the case of DAG models with latent variables. We will introduce two generalizations of DAGs, called mixed ancestor graphs (or MAGs) and partial ancestor graphs (or PAGs), and briefly describe how they can be used to search for latent variable DAG models, to classify, and to predict the effects of interventions in causal systems.
Citations
| 5662 | Probabilistic reasoning in intelligent systems: networks of plausible inference - Pearl - 1988 |
| 132 | Causal diagrams for empirical research - Pearl - 1994 |
| 59 | Learning bayesian networks: Search methods and experimental results - Chickering, Geiger, et al. - 1995 |
| 47 | Learning bayesian networks with discrete variables from data - Spirtes, Meek - 1995 |
| 20 | Bayesian model selection in structural equation models. Testing structural equation models - Raftery - 1993 |
| 20 | A discovery algorithm for directed cyclic graphs - Richardson - 1996 |
| 8 | Using d-separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors - Spirtes, Richardson, et al. - 1996 |
