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时间序列贝叶斯推断

时间序列贝叶斯推断将贝叶斯定理顺序应用于按时间排序的观测值，在每个时间步长维护隐藏状态和模型参数的完整概率分布。该框架统一了状态空间模型、动态线性模型和粒子滤波器，为滤波（实时）和回顾性平滑任务提供了校准的不确定性。

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时间序列贝叶斯推断
Bayesian Regression动态贝叶斯网络分层贝叶斯推断卡尔曼滤波器粒子滤波器（序贯蒙特卡洛）顺序蒙特卡洛动态变分推断时间序列近似贝叶斯计算时间序列贝叶斯模型平均时间序列卡尔曼滤波

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来源

  1. West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
  2. Prado, R. & West, M. (2010). Time Series: Modeling, Computation, and Inference. CRC Press. ISBN: 978-1420093360

如何引用本页

ScholarGate. (2026, June 3). Bayesian Inference for Time Series Models. ScholarGate. https://scholargate.app/zh/bayesian/time-series-bayesian-inference

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被引用于

动态变分推断时间序列近似贝叶斯计算时间序列贝叶斯模型平均时间序列卡尔曼滤波时间序列粒子滤波器时间序列变分推断

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