Salīdzināt metodes

	Dinamiskā Bayesas inferencēšana	Dinamiskais beijes tīkls
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1989–1997	1989
Autors	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)	Thomas Dean & Keiji Kanazawa
Tips	Bayesian sequential / online inference framework	probabilistic graphical model for sequences
Pirmavots	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
Citi nosaukumi	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
Saistītās	6	5
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.	A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.