方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| Metropolis-Hastings 用于模型比较× | 用于模型比较的吉布斯抽样× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1970 (extended 1995) | 1995 |
| 提出者≠ | W. K. Hastings (1970); extended for model comparison by P. J. Green (1995) | Carlin and Chib |
| 类型≠ | MCMC-based model comparison | Bayesian model selection via MCMC |
| 开创性文献≠ | 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 ↗ |
| 别名 | 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 |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|