Machine learningMachine learning
贝叶斯在线学习
贝叶斯在线学习（Bayesian online learning）以序列方式应用贝叶斯推断：每当有新的观测数据到达时，当前模型参数的后验分布就成为下一次更新的先验分布。其结果是一个原则性的概率框架，能够在整个过程中保持校准的不确定性估计，使其非常适合流式和非平稳数据设置。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Opper, M. (1998). A Bayesian approach to on-line learning. In D. Saad (Ed.), On-Line Learning in Neural Networks (pp. 363–378). Cambridge University Press. link ↗
- Sato, M. (2001). Online model selection based on the variational Bayes. Neural Computation, 13(7), 1649–1681. DOI: 10.1162/089976601750265045 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Online Learning (Sequential Posterior Update). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-online-learning
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.
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