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| 베이즈안 온라인 학습× | 베이즈 가우시안 과정× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1990s–2000s | 1978–2006 |
| 창시자≠ | Opper, M.; Sato, M. (among key contributors) | O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I. |
| 유형≠ | Probabilistic sequential learning | Probabilistic kernel model |
| 원전≠ | 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 ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 별칭 | online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL | GP regression, GPR, Gaussian process model, GP classifier |
| 관련≠ | 6 | 3 |
| 요약≠ | 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. | A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning. |
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