Bayesian methods
期望传播 (EP)
期望传播 (EP) 是一种确定性的消息传递算法，用于贝叶斯模型的近似后验推断，由 Thomas P. Minka 于 2001 年在 UAI 上提出。它迭代地优化一组局部近似因子——每个因子都来自指数族——使其乘积尽可能接近真实的难以处理的后验分布，在许多概率机器学习任务上比平均场变分推断获得更高的精度。
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来源
- Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann. link ↗
- Minka, T. P. (2001/2013). Expectation propagation for approximate Bayesian inference. arXiv:1301.2294 [cs.AI]. link ↗
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference; Section 10.7 covers Expectation Propagation.) ISBN: 978-0387310732
如何引用本页
ScholarGate. (2026, June 3). Expectation Propagation for Approximate Bayesian Inference. ScholarGate. https://scholargate.app/zh/bayesian/expectation-propagation
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