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| Confronto tra modelli mediante MCMC× | Catena di Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Famiglia | Bayesian methods | Bayesian methods |
| Anno di origine≠ | 1995 | — |
| Ideatore≠ | Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling) | — |
| Tipo≠ | Bayesian computational method | Posterior sampling algorithm |
| Fonte seminale≠ | 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 |
| Alias≠ | 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) |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. |
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