Võrdle meetodeid

	Aegridade järjestikune Monte Carlo meetod	Gibbs Sampling
Valdkond	Bayesi meetodid	Bayesi meetodid
Perekond	Bayesian methods	Bayesian methods
Tekkeaasta	1993	1984
Looja	Gordon, Salmond & Smith	Stuart Geman & Donald Geman
Tüüp	Sequential Bayesian filtering algorithm	MCMC sampling algorithm
Algallikas	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.	Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.
Rööpnimetused	particle filter, time series SMC, sequential particle filtering, bootstrap particle filter	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Seotud	5	5
Kokkuvõte	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.	Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.