So sánh phương pháp

	Suy luận biến phân chuỗi thời gian	Suy luận biến phân động (Dynamic Variational Inference)
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	1999–2017	2014–2015
Người khởi xướng	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Bayer, Osendorfer, Krishnan and colleagues
Loại	Approximate Bayesian inference	Bayesian approximate inference
Công trình gốc	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.	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.
Tên gọi khác	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
Liên quan	6	6
Tóm tắt	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).