手法を比較

	動的メトロポリス・ヘイスティングス法	Gibbs Sampling
分野	ベイズ	ベイズ
系統	Bayesian methods	Bayesian methods
提唱年	1970 (algorithm); 1992 (dynamic application)	1984
提唱者	W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer	Stuart Geman & Donald Geman
種類	Bayesian MCMC sampler for dynamic models	MCMC sampling algorithm
原典	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.
別名	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
関連	5	5
概要	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.