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| 시계열 변분 추론× | 동적 변분 추론× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1999–2017 | 2014–2015 |
| 창시자≠ | Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues | Bayer, Osendorfer, Krishnan and colleagues |
| 유형≠ | Approximate Bayesian inference | Bayesian approximate inference |
| 원전≠ | Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. DOI ↗ | Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference. link ↗ |
| 별칭 | time-series VI, variational Bayes for time series, TSVI, sequential variational inference | sequential variational inference, temporal variational inference, variational inference for state-space models, DVI |
| 관련 | 6 | 6 |
| 요약≠ | Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic latent variable models, and other time-ordered probabilistic systems. | Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a generative model of how hidden states evolve over time and a recognition network that maps observed sequences back to those latent states, optimising a sequential evidence lower bound (ELBO). |
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