手法を比較

	パーティクルフィルタ（逐次モンテカルロ法）	状態空間モデル（カルマンフィルタ）
分野	ベイズ	計量経済学
系統	Bayesian methods	Regression model
提唱年	1993	1990
提唱者	Gordon, Salmond & Smith	Harvey; Durbin & Koopman (state space treatment); Kalman filter
種類	Sequential Monte Carlo estimator	State space time series model
原典	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.
別名	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
関連	4	4
概要	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.