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| Többszintű MCMC× | Hierarchikus Bayes-féle következtetés× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 1990s | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Megalkotó≠ | Gelfand & Smith (sampling-based approach); multilevel extension developed through 1990s Bayesian hierarchical modeling literature | Lindley & Smith; Gelman et al. |
| Típus≠ | Bayesian computational inference | Bayesian multilevel model |
| Alapmű | 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 | 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 |
| Alternatív nevek | hierarchical MCMC, multilevel Bayesian sampling, MLMCMC, hierarchical Markov chain Monte Carlo | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | 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. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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