Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Réseau bayésien avec erreur de mesure× | Modélisation par équations structurelles× | |
|---|---|---|
| Domaine≠ | Bayésien | Statistiques de recherche |
| Famille≠ | Bayesian methods | Process / pipeline |
| Année d'origine≠ | 1988 (Bayesian networks); measurement-error extension: 1990s | 1921 |
| Auteur d'origine≠ | Judea Pearl (Bayesian networks); measurement-error extension developed in epidemiology and psychometrics through the 1990s–2000s | Sewall Wright |
| Type≠ | Probabilistic graphical model with latent variables | Method |
| Source fondatrice≠ | Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797 | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| Alias | BN-ME, errors-in-variables Bayesian network, Bayesian graphical model with measurement error, latent variable Bayesian network | SEM, path analysis, latent variable modeling, causal modeling |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | A Bayesian network with measurement error is a probabilistic directed acyclic graphical model in which one or more node variables are observed with error rather than exactly. Latent true-value nodes are introduced for mismeasured variables, and the model jointly infers the network's conditional probability parameters and the unobserved true values from the noisy observations. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
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