เปรียบเทียบวิธี

	อัลกอริทึม Dynamic Metropolis-Hastings	การสุ่มตัวอย่างแบบกิบบส์
สาขาวิชา	เบย์	เบย์
ตระกูล	Bayesian methods	Bayesian methods
ปีกำเนิด	1970 (algorithm); 1992 (dynamic application)	1984
ผู้ริเริ่ม	W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer	Stuart Geman & Donald Geman
ประเภท	Bayesian MCMC sampler for dynamic models	MCMC sampling algorithm
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	5
สรุป	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.