方法对比

	贝叶斯在线学习	在线学习
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1990s–2000s	1958–2000s
提出者	Opper, M.; Sato, M. (among key contributors)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
类型	Probabilistic sequential learning	Learning paradigm (sequential model update)
开创性文献	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
别名	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	incremental learning, sequential learning, streaming learning, online machine learning
相关	6	6
摘要	Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited for streaming and non-stationary data settings.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.