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	Analisi di sopravvivenza Bayesiana	Regressione di Sopravvivenza Parametrica di Weibull
Campo	Bayesiano	Analisi di sopravvivenza
Famiglia	Bayesian methods	Survival analysis
Anno di origine	2001	1951
Ideatore	Ibrahim, Chen & Sinha	Waloddi Weibull
Tipo	Bayesian time-to-event model	Fully parametric survival regression model
Fonte seminale	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Alias	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Correlati	4	4
Sintesi	Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.