Linganisha mbinu

	Uchanganuzi wa Wastani wa Mfumo wa Bayesian wa Mfululizo wa Wakati	Utohozi wa Kibayesi wa Mfululizo wa Muda
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1999–2010	1989
Mwanzilishi	Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions	Mike West and Jeff Harrison
Aina	Bayesian ensemble / model combination	Bayesian probabilistic model
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.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Majina mbadala	TS-BMA, Bayesian model averaging for time series, BMA forecasting, time series BMA	Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS
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.	Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks.