Bayesian methods
变分推断
变分推断（Variational Inference, VI）是一类将贝叶斯后验计算转化为优化问题的技术。VI 不像马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）那样从精确后验中抽取样本，而是设定一个更简单、可处理的分布族，并通过最大化证据下界（Evidence Lower BOund, ELBO）来寻找该分布族中最接近真实后验的成员。VI 由 Jordan、Ghahramani、Jaakkola 和 Saul (1999) 以现代图模型形式引入，并由 Blei、Kucukelbir 和 McAuliffe (2017) 进行了全面的统计处理，现已成为概率机器学习中标准的、可扩展的推断引擎。
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来源
- 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 ↗
- 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 ↗
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference.) ISBN: 978-0387310732
如何引用本页
ScholarGate. (2026, June 3). Variational Bayesian Inference. ScholarGate. https://scholargate.app/zh/bayesian/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
- 期望传播 (EP)贝叶斯↔ compare
- 潜在狄利克雷分配 (LDA)机器学习↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare