Võrdle meetodeid

	Konjugeeritud eelnevuse analüüs	Bayes' regressioon	Markovi ahel-Monte Carlo (MCMC)
Valdkond	Bayesi meetodid	Bayesi meetodid	Bayesi meetodid
Perekond	Bayesian methods	Bayesian methods	Bayesian methods
Tekkeaasta	1961	—	—
Looja	Raiffa & Schlaifer (1961); DeGroot (1970)	—	—
Tüüp	Closed-form Bayesian model	Bayesian linear model	Posterior sampling algorithm
Algallikas	Raiffa, H. & Schlaifer, R. (1961). Applied Statistical Decision Theory. Harvard University Press. ISBN: 978-0-87584-017-8	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
Rööpnimetused	conjugate priors, conjugate Bayesian updating, closed-form posterior analysis, Beta-Binomial model	bayesian linear regression, probabilistic regression, bayesian regresyon	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Seotud	3	2	3
Kokkuvõte	Conjugate prior analysis is a class of Bayesian inference methods in which the prior distribution and the likelihood belong to a matched family — called a conjugate pair — so that the posterior distribution has exactly the same functional form as the prior and can be derived in closed form. Introduced systematically by Raiffa and Schlaifer (1961) and consolidated by DeGroot (1970), conjugate analysis is the pedagogic backbone of introductory Bayesian statistics and a practical tool whenever analytical tractability is required.	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.