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| Mô hình phương trình cấu trúc Bayes (BSEM)× | Chuỗi Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Lĩnh vực | Bayes | Bayes |
| Họ | Bayesian methods | Bayesian methods |
| Năm ra đời≠ | 2012 | — |
| Người khởi xướng≠ | Bengt Muthén & Tihomir Asparouhov | — |
| Loại≠ | Bayesian latent variable model | Posterior sampling algorithm |
| Công trình gốc≠ | 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 |
| Tên gọi khác≠ | BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Liên quan≠ | 6 | 3 |
| Tóm tắt≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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