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| 베이즈안 온라인 학습× | 변분 추론× | |
|---|---|---|
| 분야≠ | 머신러닝 | 베이지안 |
| 계열≠ | Machine learning | Bayesian methods |
| 기원 연도≠ | 1990s–2000s | 1999 |
| 창시자≠ | Opper, M.; Sato, M. (among key contributors) | Jordan, Ghahramani, Jaakkola & Saul |
| 유형≠ | Probabilistic sequential learning | Approximate Bayesian inference |
| 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 ↗ | Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233. DOI ↗ |
| 별칭≠ | online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL | VI, variational Bayes, VB, mean-field variational inference |
| 관련≠ | 6 | 4 |
| 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. | Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning. |
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