Porovnať metódy

	Sekvenčné Monte Carlo	Gibbs Sampling
Odbor	Bayesovské metódy	Bayesovské metódy
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	1993 (particle filter); 2006 (SMC samplers)	1984
Tvorca	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)	Stuart Geman & Donald Geman
Typ	Sequential Bayesian computation	MCMC sampling algorithm
Pôvodný zdroj	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.
Ďalšie názvy	SMC, particle filter, sequential importance resampling, SMC sampler	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Príbuzné	6	5
Zhrnutie	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.	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.