Σύγκριση μεθόδων

	Μπεϋζιανή Παλινδρόμηση Cox	Γενικευμένο Γραμμικό Μοντέλο Bayes (Bayesian Generalized Linear Model)
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1972 (Cox PH); 2001 (Bayesian treatment)	1989 (GLM); 1995 (Bayesian BDA)
Δημιουργός	Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001)	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Τύπος	Survival regression	Bayesian regression model
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	Bayesian Cox PH model, Bayesian proportional hazards model, Bayesian survival regression, BCox	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Συναφείς	6	6
Σύνοψη	Bayesian Cox regression combines the Cox proportional hazards model for time-to-event data with Bayesian inference. Instead of point estimates, it produces full posterior distributions over the hazard ratios, naturally incorporating prior knowledge and providing coherent uncertainty quantification even with small samples or informative censoring.	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.