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| 동적 Metropolis-Hastings 알고리즘× | 메트로폴리스-헤이스팅스 알고리즘× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1970 (algorithm); 1992 (dynamic application) | 1953 |
| 창시자≠ | W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer | Metropolis et al. (1953); generalised by Hastings (1970) |
| 유형≠ | Bayesian MCMC sampler for dynamic models | Markov chain Monte Carlo sampler |
| 원전≠ | Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109. DOI ↗ | Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. DOI ↗ |
| 별칭≠ | Dynamic MH, MH for state-space models, Metropolis-Hastings in dynamic models, time-varying parameter MH | MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler |
| 관련 | 5 | 5 |
| 요약≠ | 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. | The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases. |
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