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| Hierarchikus Markov-lánc Monte Carlo× | Bayes-féle Regresszió× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 1990 | — |
| Megalkotó≠ | Gelfand & Smith (1990), building on Geman & Geman (1984) | — |
| Típus≠ | Bayesian computational sampler | Bayesian linear 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, MCMC for multilevel models, Bayesian hierarchical MCMC, multilevel MCMC sampling | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Kapcsolódó≠ | 6 | 2 |
| Összefoglaló≠ | Hierarchical Markov chain Monte Carlo applies MCMC sampling to hierarchical Bayesian models, jointly drawing from the posterior over both observation-level parameters and the hyperparameters that govern them. This allows principled uncertainty propagation across all levels of a multilevel structure, from individuals to groups to population, using algorithms such as Gibbs sampling, Metropolis-Hastings, or Hamiltonian Monte Carlo. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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