Linganisha mbinu

	Regressioni ya Kuishi ya Weibull ya Parametric	Uchanganuzi wa Uhai wa Bayesian
Nyanja	Uchanganuzi wa Uhai	Mbinu za Bayes
Familia	Survival analysis	Bayesian methods
Mwaka wa asili	1951	2001
Mwanzilishi	Waloddi Weibull	Ibrahim, Chen & Sinha
Aina	Fully parametric survival regression model	Bayesian time-to-event model
Chanzo asilia	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.
Majina mbadala	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model
Zinazohusiana	4	4
Muhtasari	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.	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.