Comparer des méthodes

	Dynamic Sequential Monte Carlo	Échantillonnage de Gibbs
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2006	1984
Auteur d'origine	Del Moral, Doucet, Jasra	Stuart Geman & Donald Geman
Type	Sequential Monte Carlo sampler for dynamic settings	MCMC sampling algorithm
Source fondatrice	Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.	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.
Alias	Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle sampler	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Apparentées	6	5
Résumé	Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models.	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.