Bayesian methods
自动微分变分推断 (ADVI)
自动微分变分推断 (ADVI) 是一种用于近似贝叶斯后验推断的黑盒算法，由 Kucukelbir、Tran、Ranganath、Gelman 和 Blei (2017, JMLR) 提出。对于任何可微的联合密度对数的概率模型，ADVI 自动将约束的潜在变量转换为非约束实空间，通过随机梯度上升最大化证据下界 (ELBO) 来拟合高斯变分族，并在无需模型特定推导的情况下返回近似后验。它是 Stan 中的默认变分推断引擎，也可在 PyMC 和 NumPyro 中使用。
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来源
- Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2017). Automatic differentiation variational inference. Journal of Machine Learning Research, 18(14), 1–45. link ↗
- Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2016). Automatic differentiation variational inference. arXiv:1603.00788. link ↗
- 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). Automatic Differentiation Variational Inference (ADVI). ScholarGate. https://scholargate.app/zh/bayesian/automatic-differentiation-variational-inference
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