Võrdle meetodeid

	Bayes' regressioon	Sõltumatute valimite t-test	Markovi ahel-Monte Carlo (MCMC)
Valdkond	Bayesi meetodid	Statistika	Bayesi meetodid
Perekond	Bayesian methods	Hypothesis test	Bayesian methods
Tekkeaasta	—	1908	—
Looja	—	Student (W. S. Gosset)	—
Tüüp	Bayesian linear model	Parametric mean comparison	Posterior sampling algorithm
Algallikas	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	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	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
Rööpnimetused	bayesian linear regression, probabilistic regression, bayesian regresyon	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Seotud	2	4	3
Kokkuvõte	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.	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.	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.