Confronta i metodi

	Bayes Empirico	Regressione Bayesiana	Modello a Effetti Misti
Campo	Bayesiano	Bayesiano	Statistica
Famiglia	Bayesian methods	Bayesian methods	Regression model
Anno di origine	—	—	1982
Ideatore	Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)	—	Laird & Ware
Tipo	Empirical Bayes estimator	Bayesian linear model	Mixed effects regression
Fonte seminale	Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press.	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	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
Alias	EB, empirical Bayes estimation, marginal likelihood estimation, James-Stein shrinkage	bayesian linear regression, probabilistic regression, bayesian regresyon	LME, LMM, mixed model, random effects model
Correlati	4	2	4
Sintesi	Empirical Bayes (EB) is an estimation strategy, introduced by Herbert Robbins in 1956 and developed into practical shrinkage estimators by Bradley Efron and Carl Morris in 1973, in which the hyperparameters of the prior distribution are estimated from the observed data via the marginal likelihood rather than specified in advance. The resulting posterior retains a Bayesian structure but substitutes data-driven hyperparameters for subjective ones, bridging frequentist shrinkage and full Bayesian 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.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.