Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| MCMC para Comparação de Modelos× | Amostragem de Gibbs× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1995 | 1984 |
| Autor original≠ | Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling) | Stuart Geman & Donald Geman |
| Tipo≠ | Bayesian computational method | MCMC sampling algorithm |
| Fonte seminal≠ | Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732. DOI ↗ | Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741. DOI ↗ |
| Outros nomes | reversible-jump MCMC, RJMCMC, marginal likelihood estimation via MCMC, Bayesian model selection via MCMC | Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form. |
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