Bayesian methodsBayesian / computational
Gibbs Sampling
Gibbs采样是一种马尔可夫链蒙特卡洛算法，它通过从给定所有其他参数和数据的完整条件分布中反复抽取每个参数来近似高维后验分布。由于每次抽取都是从条件分布中精确抽取的——而不是可能被拒绝的提议——因此当这些条件分布可以解析获得时，该采样器效率很高。
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来源
- 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: 10.1109/TPAMI.1984.4767596 ↗
- Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398-409. DOI: 10.1080/01621459.1990.10476213 ↗
如何引用本页
ScholarGate. (2026, June 3). Gibbs Sampling Markov Chain Monte Carlo. ScholarGate. https://scholargate.app/zh/bayesian/gibbs-sampling
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.
- Bayesian Regression贝叶斯↔ compare
- Hamiltonian Monte Carlo贝叶斯↔ compare
- 分层贝叶斯推断贝叶斯↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 变分推断贝叶斯↔ compare
被引用于
缺失数据的贝叶斯推断动态哈密顿蒙特卡洛动态 Metropolis-Hastings 算法动态蒙特卡洛模拟动态序贯蒙特卡洛法用于模型比较的吉布斯抽样含测量误差的Gibbs采样带缺失数据的吉布斯抽样分层贝叶斯推断分层自助法模拟分层马尔可夫链蒙特卡洛用于模型比较的MCMC含测量误差的MCMC缺失数据下的MCMCMetropolis-Hastings算法多层贝叶斯模型平均多层自助法模拟多层吉布斯采样多层级 MCMC鲁棒吉布斯采样鲁棒哈密顿蒙特卡洛稳健马尔可夫链蒙特卡洛 (Robust Markov Chain Monte Carlo)顺序蒙特卡洛切片采样空间吉布斯采样空间马尔可夫链蒙特卡洛 (Spatial MCMC)空间蒙特卡洛模拟时间序列 MCMC时间序列序列蒙特卡洛方法