Linganisha mbinu

	Uchanganuzi wa Wastani wa Mfumo wa Bayesian wa Mfululizo wa Wakati	Monte Carlo Sekwenshiali
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1999–2010	1993 (particle filter); 2006 (SMC samplers)
Mwanzilishi	Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Aina	Bayesian ensemble / model combination	Sequential Bayesian computation
Chanzo asilia	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	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	TS-BMA, Bayesian model averaging for time series, BMA forecasting, time series BMA	SMC, particle filter, sequential importance resampling, SMC sampler
Zinazohusiana	5	6
Muhtasari	Time series Bayesian model averaging (TS-BMA) combines forecasts from an ensemble of time series models — such as AR, VAR, or state-space specifications — by weighting each model by its posterior probability given observed data. Rather than selecting one model and discarding uncertainty about which model is best, TS-BMA integrates over model uncertainty, producing forecasts that are more robust and better calibrated than any single model alone.	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.