Vertaile menetelmiä
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| Partikkelisuodin (sekventiaalinen Monte Carlo)× | Tilamallinnus (Kalman-suodin)× | |
|---|---|---|
| Tieteenala≠ | Bayesilainen tilastotiede | Ekonometria |
| Menetelmäperhe≠ | Bayesian methods | Regression model |
| Syntyvuosi≠ | 1993 | 1990 |
| Kehittäjä≠ | Gordon, Salmond & Smith | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Tyyppi≠ | Sequential Monte Carlo estimator | State space time series model |
| Alkuperäislähde≠ | 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. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Rinnakkaisnimet≠ | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | 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. |
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