Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Багаторівнева бутстреп-симуляція× | Багаторівневий MCMC× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 1979 (bootstrap); multilevel variants c.1990s | 1990s |
| Автор методу≠ | Efron (1979); multilevel extensions developed through 1980s–2000s | Gelfand & Smith (sampling-based approach); multilevel extension developed through 1990s Bayesian hierarchical modeling literature |
| Тип≠ | resampling / simulation | Bayesian computational inference |
| Основоположне джерело≠ | Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26. DOI ↗ | 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 |
| Інші назви | hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling | hierarchical MCMC, multilevel Bayesian sampling, MLMCMC, hierarchical Markov chain Monte Carlo |
| Пов'язані | 6 | 6 |
| Підсумок≠ | Multilevel bootstrap simulation is a resampling technique designed for clustered or hierarchically structured data. It preserves the nested data structure by resampling at each level independently — first drawing clusters (e.g., schools, hospitals), then drawing observations within each sampled cluster — so that bootstrap replicate datasets reflect the same multilevel organisation as the original data. | Multilevel MCMC applies Markov chain Monte Carlo sampling to hierarchical (multilevel) Bayesian models. It draws samples from the joint posterior of both group-level and population-level parameters simultaneously, propagating uncertainty across levels and enabling inference in clustered or nested data structures where observations within groups share common distributional characteristics. |
| ScholarGateНабір даних ↗ |
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