Porovnat metody

	Robustní částicový filtr	Částicový filtr (sekvenční Monte Carlo)
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	1998-2004	1993
Tvůrce	Hurzeler & Kunsch; Ristic, Arulampalam & Gordon	Gordon, Salmond & Smith
Typ	Sequential Bayesian estimation	Sequential Monte Carlo estimator
Původní zdroj	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.
Další názvy	RPF, robust sequential Monte Carlo, outlier-robust particle filter, heavy-tailed particle filter	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Příbuzné	6	4
Shrnutí	The Robust Particle Filter is a sequential Monte Carlo method that tracks hidden states in nonlinear, non-Gaussian systems while remaining resistant to outliers and model misspecification. It replaces the standard Gaussian likelihood with a heavy-tailed or bounded-influence density, so that anomalous observations receive downweighted importance and cannot derail the state estimate.	The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.