Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Metropolis-Hastings para Comparação de Modelos× | MCMC para Comparação de Modelos× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1970 (extended 1995) | 1995 |
| Autor original≠ | W. K. Hastings (1970); extended for model comparison by P. J. Green (1995) | Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling) |
| Tipo≠ | MCMC-based model comparison | Bayesian computational method |
| Fonte seminal≠ | Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109. DOI ↗ | Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732. DOI ↗ |
| Outros nomes | MH model comparison, Metropolis-Hastings Bayes factor estimation, reversible-jump Metropolis-Hastings, MH model selection | reversible-jump MCMC, RJMCMC, marginal likelihood estimation via MCMC, Bayesian model selection via MCMC |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | Metropolis-Hastings for model comparison uses the Metropolis-Hastings MCMC algorithm to explore both parameter and model space simultaneously, producing posterior probabilities for competing models and enabling Bayes factor estimation without requiring closed-form marginal likelihoods. The canonical extension — reversible-jump MCMC by Green (1995) — handles models of different dimensionalities within a single sampler. | 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. |
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