Compara mètodes

	Anàlisi de priors conjugats	Bayes Empíric
Camp	Bayesià	Bayesià
Família	Bayesian methods	Bayesian methods
Any d'origen	1961	—
Autor original	Raiffa & Schlaifer (1961); DeGroot (1970)	Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)
Tipus	Closed-form Bayesian model	Empirical Bayes estimator
Font seminal	Raiffa, H. & Schlaifer, R. (1961). Applied Statistical Decision Theory. Harvard University Press. ISBN: 978-0-87584-017-8	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.
Àlies	conjugate priors, conjugate Bayesian updating, closed-form posterior analysis, Beta-Binomial model	EB, empirical Bayes estimation, marginal likelihood estimation, James-Stein shrinkage
Relacionats	3	4
Resum	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.	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.