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时间序列变分推断

时间序列变分推断将变分贝叶斯方法应用于序列数据，用一个可处理的分布族来近似潜状态和参数上难以处理的后验分布。通过最大化证据下界（ELBO），它为状态空间模型、动态潜变量模型和其他时间有序的概率系统提供了快速、可扩展的贝叶斯推断。

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时间序列变分推断
动态变分推断卡尔曼滤波器顺序蒙特卡洛时间序列贝叶斯推断时间序列 MCMC变分推断

来源

  1. Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. DOI: 10.1080/01621459.2017.1285773 ↗
  2. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S. & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183-233. DOI: 10.1023/A:1007665907178 ↗

如何引用本页

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

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