Salīdzināt metodes

	Dinamiskā variācijas izskaidrošana	Particle Filter (Sequential Monte Carlo)
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2014–2015	1993
Autors	Bayer, Osendorfer, Krishnan and colleagues	Gordon, Salmond & Smith
Tips	Bayesian approximate inference	Sequential Monte Carlo estimator
Pirmavots	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.
Citi nosaukumi	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Saistītās	6	4
Kopsavilkums	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).	The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.