Salīdzināt metodes

	Dinamiskais Metropolis-Hastingsa algoritms	Metropolis-Hastings algoritms
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1970 (algorithm); 1992 (dynamic application)	1953
Autors	W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer	Metropolis et al. (1953); generalised by Hastings (1970)
Tips	Bayesian MCMC sampler for dynamic models	Markov chain Monte Carlo sampler
Pirmavots	Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.	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.
Citi nosaukumi	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
Saistītās	5	5
Kopsavilkums	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.