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| Динамично Байесово извеждане× | Последователен Монте Карло× | |
|---|---|---|
| Област | Бейсови методи | Бейсови методи |
| Семейство | Bayesian methods | Bayesian methods |
| Година на възникване≠ | 1989–1997 | 1993 (particle filter); 2006 (SMC samplers) |
| Създател≠ | West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Тип≠ | Bayesian sequential / online inference framework | Sequential Bayesian computation |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating | SMC, particle filter, sequential importance resampling, SMC sampler |
| Свързани | 6 | 6 |
| Резюме≠ | Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time. | 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. |
| ScholarGateНабор от данни ↗ |
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