Bayesian methodsBayesian / computational
时间序列 MCMC
时间序列 MCMC 将马尔可夫链蒙特卡洛方法应用于按时间排序的数据上的贝叶斯推断。它不是优化单一参数估计值，而是从参数和潜在状态的完整联合后验分布中抽取样本，从而产生概率分布，这些分布能够诚实地反映动力学、趋势和每个时间点的季节性模式方面的不确定性。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553. DOI: 10.1093/biomet/81.3.541 ↗
- West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
如何引用本页
ScholarGate. (2026, June 3). Markov Chain Monte Carlo for Time Series Models. ScholarGate. https://scholargate.app/zh/bayesian/time-series-mcmc
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.
- 动态贝叶斯推断贝叶斯↔ compare
- Gibbs Sampling贝叶斯↔ compare
- Hamiltonian Monte Carlo贝叶斯↔ compare
- 卡尔曼滤波器贝叶斯↔ compare
- 粒子滤波器（序贯蒙特卡洛）贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare