Linganisha mbinu

	Uchujaji wa vipande (Slice Sampling)	Usajili wa Bayesian	Markov Chain Monte Carlo (MCMC)
Nyanja	Mbinu za Bayes	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods	Bayesian methods
Mwaka wa asili	2003	—	—
Mwanzilishi	Radford M. Neal	—	—
Aina	MCMC sampling algorithm	Bayesian linear model	Posterior sampling algorithm
Chanzo asilia	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	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
Majina mbadala	slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler	bayesian linear regression, probabilistic regression, bayesian regresyon	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Zinazohusiana	4	2	3
Muhtasari	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.	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.