পদ্ধতির তুলনা করুন

	Particle Filter (Sequential Monte Carlo)	স্টেট স্পেস মডেল (কালম্যান ফিল্টার)
ক্ষেত্র	বেইসীয়	অর্থমিতি
পরিবার	Bayesian methods	Regression model
উদ্ভবের বছর	1993	1990
প্রবর্তক	Gordon, Salmond & Smith	Harvey; Durbin & Koopman (state space treatment); Kalman filter
ধরন	Sequential Monte Carlo estimator	State space time series model
মৌলিক উৎস	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.
অপর নাম	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
সম্পর্কিত	4	4
সারসংক্ষেপ	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.