Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| MCMC pro porovnání modelů× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|
| Obor | Bayesovská statistika | Bayesovská statistika |
| Rodina | Bayesian methods | Bayesian methods |
| Rok vzniku≠ | 1995 | — |
| Tvůrce≠ | Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling) | — |
| Typ≠ | Bayesian computational method | Posterior sampling algorithm |
| Původní zdroj≠ | Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732. 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≠ | reversible-jump MCMC, RJMCMC, marginal likelihood estimation via MCMC, Bayesian model selection via MCMC | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Příbuzné≠ | 5 | 3 |
| Shrnutí≠ | MCMC for model comparison uses Markov chain Monte Carlo algorithms to estimate the marginal likelihoods and Bayes factors needed to formally compare competing statistical models. Techniques such as reversible-jump MCMC and bridge sampling allow exploration across model spaces of different dimensionality, enabling fully Bayesian model selection and averaging. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
| ScholarGateDatová sada ↗ |
|
|