Salīdzināt metodes

	Dinamiskais beijes tīkls	Particle Filter (Sequential Monte Carlo)
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1989	1993
Autors	Thomas Dean & Keiji Kanazawa	Gordon, Salmond & Smith
Tips	probabilistic graphical model for sequences	Sequential Monte Carlo estimator
Pirmavots	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.	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	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Saistītās	5	4
Kopsavilkums	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.	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.