Salīdzināt metodes

	Time series variational inference	Dinamiskā variācijas izskaidrošana
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1999–2017	2014–2015
Autors	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Bayer, Osendorfer, Krishnan and colleagues
Tips	Approximate Bayesian inference	Bayesian approximate inference
Pirmavots	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.
Citi nosaukumi	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
Saistītās	6	6
Kopsavilkums	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).