Sammenlign metoder

	Bayesiansk overlevelsesanalyse	Bayesiansk regresjon	Weibull parametrisk overlevelsesregresjon
Fagfelt	Bayesiansk	Bayesiansk	Overlevelsesanalyse
Familie	Bayesian methods	Bayesian methods	Survival analysis
Opprinnelsesår	2001	—	1951
Opphavsperson	Ibrahim, Chen & Sinha	—	Waloddi Weibull
Type	Bayesian time-to-event model	Bayesian linear model	Fully parametric survival regression model
Opprinnelig kilde	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.	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	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	bayesian linear regression, probabilistic regression, bayesian regresyon	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Relaterte	4	2	4
Sammendrag	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	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.