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| Μπεϋζιανή Μοντελοποίηση Δομικών Εξισώσεων (BSEM)× | Μπεϋζιανή Παλινδρόμηση× | |
|---|---|---|
| Πεδίο | Μπεϋζιανή Στατιστική | Μπεϋζιανή Στατιστική |
| Οικογένεια | Bayesian methods | Bayesian methods |
| Έτος προέλευσης≠ | 2012 | — |
| Δημιουργός≠ | Bengt Muthén & Tihomir Asparouhov | — |
| Τύπος≠ | Bayesian latent variable model | Bayesian linear model |
| Θεμελιώδης πηγή≠ | Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335. link ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Εναλλακτικές ονομασίες≠ | BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Συναφείς≠ | 6 | 2 |
| Σύνοψη≠ | Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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