Vertaile menetelmiä

	Aikasarjojen sekventiaalinen Monte Carlo -simulointi	Dynaaminen Bayesilainen Verkko
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	1993	1989
Kehittäjä	Gordon, Salmond & Smith	Thomas Dean & Keiji Kanazawa
Tyyppi	Sequential Bayesian filtering algorithm	probabilistic graphical model for sequences
Alkuperäislähde	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.	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
Rinnakkaisnimet	particle filter, time series SMC, sequential particle filtering, bootstrap particle filter	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
Liittyvät	5	5
Tiivistelmä	Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.	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.