Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Hamiltonovské Monte Carlo× | Bayesovská regrese× | |
|---|---|---|
| Obor | Bayesovská statistika | Bayesovská statistika |
| Rodina | Bayesian methods | Bayesian methods |
| Rok vzniku≠ | 1987 | — |
| Tvůrce | — | — |
| Typ≠ | Gradient-based Markov chain Monte Carlo sampler | Bayesian linear model |
| Původní zdroj≠ | Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222. 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≠ | HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Příbuzné≠ | 3 | 2 |
| Shrnutí≠ | Hamiltonian Monte Carlo (HMC) is a gradient-based Markov chain Monte Carlo algorithm that uses the geometry of the log-posterior surface to make large, informed jumps through parameter space instead of the small random steps of classical MCMC. Originally introduced for lattice field theory by Duane, Kennedy, Pendleton, and Roweth (1987) under the name Hybrid Monte Carlo, and brought into mainstream statistics by Radford Neal's authoritative 2011 chapter, HMC is today the default sampler in Stan and PyMC and is widely regarded as the state-of-the-art engine for Bayesian posterior inference in high-dimensional models. | 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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