Bayesian methodsBayesian / computational
带缺失数据变分推断
带缺失数据变分推断是一种可扩展的贝叶斯方法，它在填补缺失观测值的同时，近似推断潜在变量和模型参数的后验分布。它不精确地对缺失条目的所有可能值进行积分，而是设定一个可处理的近似分布，并优化该分布使其尽可能接近真实的联合后验分布，从而在处理高维不完整数据集时也能实现快速、有原则的推断。
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来源
- Ghahramani, Z. & Jordan, M. I. (1994). Supervised learning from incomplete data via an EM approach. In Cowan, J. D., Tesauro, G. & Alspector, J. (Eds.), Advances in Neural Information Processing Systems 6 (pp. 120–127). Morgan Kaufmann. link ↗
- Wainwright, M. J. & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1–2), 1–305. DOI: 10.1561/2200000001 ↗
如何引用本页
ScholarGate. (2026, June 3). Variational Bayesian Inference with Missing Data. ScholarGate. https://scholargate.app/zh/bayesian/variational-inference-with-missing-data
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