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时间序列贝叶斯分层模型

时间序列贝叶斯分层模型将分层（多层）贝叶斯框架与动态状态空间结构相结合，用于分析在多个单元或组上收集的时间数据。先验编码了关于单元内动态和跨单元变异的信念，后验通过MCMC或序贯蒙特卡洛获得，从而产生具有校准不确定性的完整概率预测。

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时间序列贝叶斯分层模型
Bayesian Regression动态贝叶斯网络分层贝叶斯推断卡尔曼滤波器多层贝叶斯推断时间序列 MCMC

来源

  1. West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
  2. 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). Time Series Bayesian Hierarchical Model. ScholarGate. https://scholargate.app/zh/bayesian/time-series-bayesian-hierarchical-model

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ScholarGate — Time series Bayesian hierarchical model (Time Series Bayesian Hierarchical Model). 于 2026-06-15 检索自 https://scholargate.app/zh/bayesian/time-series-bayesian-hierarchical-model · 数据集： https://doi.org/10.5281/zenodo.20539026