Usporedite metode

	Metoda uzorkovanja po odsječcima	Gibbs uzorkovanje	Markovova lančana Monte Carlo (MCMC)
Područje	Bayesovska statistika	Bayesovska statistika	Bayesovska statistika
Obitelj	Bayesian methods	Bayesian methods	Bayesian methods
Godina nastanka	2003	1984	—
Tvorac	Radford M. Neal	Stuart Geman & Donald Geman	—
Vrsta	MCMC sampling algorithm	MCMC sampling algorithm	Posterior sampling algorithm
Temeljni izvor	Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767.	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.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Drugi nazivi	slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Srodne	4	5	3
Sažetak	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.	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.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.