Confronta i metodi

	Regressione di Sopravvivenza Bayesiana	Modello Lineare Generalizzato Bayesiano
Campo	Statistica	Statistica
Famiglia	Regression model	Regression model
Anno di origine	1990s–2001	1989 (GLM); 1995 (Bayesian BDA)
Ideatore	Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al.	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Tipo	Bayesian parametric/semiparametric regression	Bayesian regression model
Fonte seminale	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	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
Alias	Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Correlati	5	6
Sintesi	Bayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects.	A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.