השוואת שיטות

	פילטר חלקיקים (מונטה קרלו סדרתי)	מודל מרחב מצב (מסנן קלמן)
תחום	בייסיאני	אקונומטריקה
משפחה	Bayesian methods	Regression model
שנת המקור	1993	1990
הוגה השיטה	Gordon, Salmond & Smith	Harvey; Durbin & Koopman (state space treatment); Kalman filter
סוג	Sequential Monte Carlo estimator	State space time series model
מקור מכונן	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.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
כינויים	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
קשורות	4	4
תקציר	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.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.