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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Байесов моделиране на структурни уравнения (BSEM)× | Марковски Монте Карло вериги (MCMC)× | |
|---|---|---|
| Област | Бейсови методи | Бейсови методи |
| Семейство | Bayesian methods | Bayesian methods |
| Година на възникване≠ | 2012 | — |
| Създател≠ | Bengt Muthén & Tihomir Asparouhov | — |
| Тип≠ | Bayesian latent variable model | Posterior sampling algorithm |
| Основополагащ източник≠ | 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 |
| Други названия≠ | BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Свързани≠ | 6 | 3 |
| Резюме≠ | 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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