Porovnat metody

	Bayesovská regrese	Hierarchické Bayesovské odvozování	Markov Chain Monte Carlo (MCMC)
Obor	Bayesovská statistika	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods	Bayesian methods
Rok vzniku	—	1972 (Lindley & Smith); consolidated 1995–2013	—
Tvůrce	—	Lindley & Smith; Gelman et al.	—
Typ	Bayesian linear model	Bayesian multilevel model	Posterior sampling algorithm
Původní zdroj	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	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
Další názvy	bayesian linear regression, probabilistic regression, bayesian regresyon	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Příbuzné	2	6	3
Shrnutí	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.	Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.	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.