Comparar métodos

	Inferencia Variacional de Series Temporales	Monte Carlo Secuencial
Campo	Bayesiano	Bayesiano
Familia	Bayesian methods	Bayesian methods
Año de origen	1999–2017	1993 (particle filter); 2006 (SMC samplers)
Autor original	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tipo	Approximate Bayesian inference	Sequential Bayesian computation
Fuente seminal	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.	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.
Alias	time-series VI, variational Bayes for time series, TSVI, sequential variational inference	SMC, particle filter, sequential importance resampling, SMC sampler
Relacionados	6	6
Resumen	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.	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.