Salīdzināt metodes

	Metropolis-Hastings algoritms	Šlūžu izlase
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1953	2003
Autors	Metropolis et al. (1953); generalised by Hastings (1970)	Radford M. Neal
Tips	Markov chain Monte Carlo sampler	MCMC sampling algorithm
Pirmavots	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.	Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767.
Citi nosaukumi	MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler	slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler
Saistītās	5	4
Kopsavilkums	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.	Slice sampling is a Markov chain Monte Carlo (MCMC) algorithm introduced by Radford M. Neal in his 2003 Annals of Statistics paper. It generates samples from a target distribution by drawing uniformly from the region under the density curve — called the 'slice' — without requiring the user to specify a step-size or proposal distribution, making it self-tuning and broadly applicable for Bayesian posterior inference.