Comparer des méthodes

	Slice Sampling	Régression bayésienne
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2003	—
Auteur d'origine	Radford M. Neal	—
Type	MCMC sampling algorithm	Bayesian linear model
Source fondatrice	Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767.	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
Alias	slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler	bayesian linear regression, probabilistic regression, bayesian regresyon
Apparentées	4	2
Résumé	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.