Linganisha mbinu

	Mbinu ya Monte Carlo ya Mlolongo Imara	Monte Carlo Sekwenshiali
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	2000s	1993 (particle filter); 2006 (SMC samplers)
Mwanzilishi	Ristic, Arulampalam, Gordon and others (2000s, with ongoing development)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Aina	Sequential Bayesian sampling algorithm	Sequential Bayesian computation
Chanzo asilia	Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318	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	robust particle filter, robust SMC, outlier-robust particle filtering, heavy-tailed SMC	SMC, particle filter, sequential importance resampling, SMC sampler
Zinazohusiana	6	6
Muhtasari	Robust Sequential Monte Carlo (Robust SMC) extends standard particle filtering to handle outliers, heavy-tailed noise, and model misspecification in sequential data. By replacing Gaussian likelihood assumptions with heavier-tailed distributions or employing outlier-detection strategies during particle weighting, it maintains accurate state-tracking and parameter estimation even when observations deviate from the assumed model.	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.