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| Bayesowskie wnioskowanie w szeregach czasowych× | Sekwencyjne metody Monte Carlo× | |
|---|---|---|
| Dziedzina | Statystyka bayesowska | Statystyka bayesowska |
| Rodzina | Bayesian methods | Bayesian methods |
| Rok powstania≠ | 1989 | 1993 (particle filter); 2006 (SMC samplers) |
| Twórca≠ | Mike West and Jeff Harrison | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Typ≠ | Bayesian probabilistic model | Sequential Bayesian computation |
| Źródło pierwotne≠ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | 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 ↗ |
| Inne nazwy | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS | SMC, particle filter, sequential importance resampling, SMC sampler |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks. | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. |
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