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	Markov Chain Monte Carlo (MCMC)	Bayesiansk regression
Ämnesområde	Simulering	Bayesiansk statistik
Familj	Process / pipeline	Bayesian methods
Ursprungsår	1953 (Metropolis-Hastings); 1984 (Gibbs)	—
Upphovsperson	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)	—
Typ	Simulation-based Bayesian inference / numerical integration	Bayesian linear model
Ursprungskälla	Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.	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	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)	bayesian linear regression, probabilistic regression, bayesian regresyon
Närliggande	5	2
Sammanfattning	Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.	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.