قارن الطرق

	خوارزمية متروبوليس-هاستينغز الديناميكية	أخذ العينات بطريقة جيبس
المجال	بايزي	بايزي
العائلة	Bayesian methods	Bayesian methods
سنة النشأة	1970 (algorithm); 1992 (dynamic application)	1984
صاحب الطريقة	W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer	Stuart Geman & Donald Geman
النوع	Bayesian MCMC sampler for dynamic models	MCMC sampling algorithm
المصدر التأسيسي	Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.	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.
الأسماء البديلة	Dynamic MH, MH for state-space models, Metropolis-Hastings in dynamic models, time-varying parameter MH	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
ذات صلة	5	5
الملخص	The Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, latent states or evolving parameters are updated via proposal-and-accept moves, yielding full posterior distributions over trajectories rather than single filtered estimates.	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.