Salīdzināt metodes

	Dinamiskā Bayesas inferencēšana	Particle Filter (Sequential Monte Carlo)
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1989–1997	1993
Autors	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)	Gordon, Salmond & Smith
Tips	Bayesian sequential / online inference framework	Sequential Monte Carlo estimator
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, sequential Monte Carlo, bootstrap filter, condensation algorithm
Saistītās	6	4
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.	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.