Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Algoritmus Metropolis-Hastings× | Bayesovská regrese× | |
|---|---|---|
| Obor | Bayesovská statistika | Bayesovská statistika |
| Rodina | Bayesian methods | Bayesian methods |
| Rok vzniku≠ | 1953 | — |
| Tvůrce≠ | Metropolis et al. (1953); generalised by Hastings (1970) | — |
| Typ≠ | Markov chain Monte Carlo sampler | Bayesian linear model |
| Původní zdroj≠ | Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. 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 |
| Další názvy≠ | MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Příbuzné≠ | 5 | 2 |
| Shrnutí≠ | The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases. | 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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