Linganisha mbinu

	Uchanganuzi wa dhahania wa mfululizo wa wakati	Monte Carlo Sekwenshiali
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1999–2017	1993 (particle filter); 2006 (SMC samplers)
Mwanzilishi	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Aina	Approximate Bayesian inference	Sequential Bayesian computation
Chanzo asilia	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.
Majina mbadala	time-series VI, variational Bayes for time series, TSVI, sequential variational inference	SMC, particle filter, sequential importance resampling, SMC sampler
Zinazohusiana	6	6
Muhtasari	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.