Bayesian methodsBayesian / computational
用于模型比较的MCMC
用于模型比较的马尔可夫链蒙特卡洛（MCMC）算法估计边际似然和贝叶斯因子，这些是正式比较竞争统计模型所必需的。可逆跳MCMC和桥采样等技术允许在不同维度的模型空间中进行探索，从而实现完全贝叶斯模型选择和平均。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732. DOI: 10.1093/biomet/82.4.711 ↗
- Meng, X.-L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6(4), 831–860. link ↗
如何引用本页
ScholarGate. (2026, June 3). Markov Chain Monte Carlo for Bayesian Model Comparison. ScholarGate. https://scholargate.app/zh/bayesian/mcmc-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.
- 近似贝叶斯计算仿真↔ compare
- 贝叶斯模型平均 (Bayesian Model Averaging, BMA)贝叶斯↔ compare
- Gibbs Sampling贝叶斯↔ compare
- Hamiltonian Monte Carlo贝叶斯↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare