Módszerek összehasonlítása
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| Metropolis-Hastings modellösszehasonlítás× | Gibbs-mintavételezés modellösszehasonlításhoz× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 1970 (extended 1995) | 1995 |
| Megalkotó≠ | W. K. Hastings (1970); extended for model comparison by P. J. Green (1995) | Carlin and Chib |
| Típus≠ | MCMC-based model comparison | Bayesian model selection via MCMC |
| Alapmű≠ | Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109. DOI ↗ | Carlin, B. P. & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 57(3), 473-484. DOI ↗ |
| Alternatív nevek | MH model comparison, Metropolis-Hastings Bayes factor estimation, reversible-jump Metropolis-Hastings, MH model selection | Gibbs-based model selection, MCMC model comparison via Gibbs, Bayesian model comparison with Gibbs sampling, Gibbs sampler model selection |
| Kapcsolódó≠ | 4 | 3 |
| Összefoglaló≠ | 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. | Gibbs sampling for model comparison is a Bayesian MCMC approach that simultaneously samples from the space of competing models and their parameters. By augmenting the Gibbs sampler with a discrete model-index variable, posterior model probabilities and Bayes factors are estimated from the resulting Markov chain without requiring separate runs per model. |
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