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动态变分推断

动态变分推断将变分推断框架扩展到序列和时间序列设置，通过设定一个结构化的近似后验，该后验尊重潜在状态的时间顺序。它联合学习一个隐藏状态随时间演变的生成模型和一个将观测序列映射回这些潜在状态的识别网络，并优化一个序列证据下界（ELBO）。

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动态变分推断
动态贝叶斯网络卡尔曼滤波器粒子滤波器（序贯蒙特卡洛）顺序蒙特卡洛时间序列贝叶斯推断变分推断动态贝叶斯模型平均时间序列变分推断

来源

  1. Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference. link ↗
  2. Bayer, J., & Osendorfer, C. (2014). Learning Stochastic Recurrent Networks. NIPS 2014 Workshop on Advances in Variational Inference. link ↗

如何引用本页

ScholarGate. (2026, June 3). Dynamic Variational Inference for Sequential Latent Variable Models. ScholarGate. https://scholargate.app/zh/bayesian/dynamic-variational-inference

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动态贝叶斯模型平均时间序列变分推断

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ScholarGate — Dynamic Variational Inference (Dynamic Variational Inference for Sequential Latent Variable Models). 于 2026-06-15 检索自 https://scholargate.app/zh/bayesian/dynamic-variational-inference · 数据集： https://doi.org/10.5281/zenodo.20539026