方法对比
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| 贝叶斯因子分析× | 马尔可夫链蒙特卡洛 (MCMC)× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 2004 | — |
| 提出者≠ | Lopes & West (2004) for Bayesian model assessment in factor analysis | — |
| 类型≠ | Bayesian latent variable model | Posterior sampling algorithm |
| 开创性文献≠ | Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67. 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 |
| 别名≠ | Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| 相关≠ | 7 | 3 |
| 摘要≠ | Bayesian Factor Analysis is a probabilistic latent-variable method that places prior distributions on the factor loading matrix and the residual variances, then infers a full posterior over these parameters from the observed data. Developed prominently in the Bayesian framework by Lopes and West (2004), it extends classical exploratory and confirmatory factor analysis by quantifying uncertainty in every estimated loading rather than reporting single point estimates. | 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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