Compara mètodes

	Inferencia Variacional Dinàmica	Monte Carlo Seqüencial
Camp	Bayesià	Bayesià
Família	Bayesian methods	Bayesian methods
Any d'origen	2014–2015	1993 (particle filter); 2006 (SMC samplers)
Autor original	Bayer, Osendorfer, Krishnan and colleagues	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tipus	Bayesian approximate inference	Sequential Bayesian computation
Font seminal	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.
Àlies	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI	SMC, particle filter, sequential importance resampling, SMC sampler
Relacionats	6	6
Resum	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).	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.