Salīdzināt metodes

	Dinamiskā Bayesas inferencēšana	Sekvenciālā Monte Karlo metode
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1989–1997	1993 (particle filter); 2006 (SMC samplers)
Autors	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tips	Bayesian sequential / online inference framework	Sequential Bayesian computation
Pirmavots	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.
Citi nosaukumi	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating	SMC, particle filter, sequential importance resampling, SMC sampler
Saistītās	6	6
Kopsavilkums	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.