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	बायेसियन सर्वाइवल रिग्रेशन	सर्वाइवल रिग्रेशन (Survival Regression)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1990s–2001	1980s
प्रवर्तक	Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al.	Kalbfleisch & Prentice; Cox & Oakes
प्रकार	Bayesian parametric/semiparametric regression	Parametric survival model
मौलिक स्रोत	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576
उपनाम	Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model	accelerated failure time model, AFT model, parametric survival model, time-to-event regression
संबंधित	5	3
सारांश	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.	Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.