Bayesian methodsBayesian / computational
分层变分推断
分层变分推断（Hierarchical Variational Inference, HVI）通过在变分族本身之上放置更丰富的分层结构来扩展标准变分推断。HVI不使用简单的均值场近似，而是引入辅助潜在变量来捕捉主要潜在变量之间的依赖关系，从而为复杂的贝叶斯模型产生更紧的证据下界（evidence lower bound, ELBO）和更精确的后验近似。
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来源
- Ranganath, R., Altosaar, J., Tran, D. & Blei, D. M. (2016). Hierarchical Variational Models. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), PMLR 48, 324-333. link ↗
- 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). Hierarchical Variational Inference. ScholarGate. https://scholargate.app/zh/bayesian/hierarchical-variational-inference
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bayesian Regression贝叶斯↔ compare
- 分层贝叶斯推断贝叶斯↔ compare
- 分层马尔可夫链蒙特卡洛贝叶斯↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 变分推断贝叶斯↔ compare