Bayesian methodsBayesian / computational
用于模型比较的吉布斯抽样
用于模型比较的吉布斯抽样（Gibbs sampling for model comparison）是一种贝叶斯马尔可夫链蒙特卡洛（MCMC）方法，它能同时从竞争模型及其参数空间中进行抽样。通过为吉布斯抽样器增加一个离散的模型索引变量，可以从生成的马尔可夫链中估计后验模型概率和贝叶斯因子，而无需为每个模型单独运行。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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: 10.1111/j.2517-6161.1995.tb02042.x ↗
- 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
如何引用本页
ScholarGate. (2026, June 3). Gibbs Sampling for Bayesian Model Comparison. ScholarGate. https://scholargate.app/zh/bayesian/gibbs-sampling-for-model-comparison
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 贝叶斯模型平均 (Bayesian Model Averaging, BMA)贝叶斯↔ compare
- Gibbs Sampling贝叶斯↔ compare
- Metropolis-Hastings 用于模型比较贝叶斯↔ compare