Vertaile menetelmiä

	Robust Sequential Monte Carlo	Sekventiaalinen Monte Carlo
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	2000s	1993 (particle filter); 2006 (SMC samplers)
Kehittäjä	Ristic, Arulampalam, Gordon and others (2000s, with ongoing development)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tyyppi	Sequential Bayesian sampling algorithm	Sequential Bayesian computation
Alkuperäislähde	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.
Rinnakkaisnimet	robust particle filter, robust SMC, outlier-robust particle filtering, heavy-tailed SMC	SMC, particle filter, sequential importance resampling, SMC sampler
Liittyvät	6	6
Tiivistelmä	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.