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| 베이지안 생존 분석× | 베이즈 회귀× | Weibull 모수 생존 회귀분석× | |
|---|---|---|---|
| 분야≠ | 베이지안 | 베이지안 | 생존분석 |
| 계열≠ | Bayesian methods | Bayesian methods | Survival analysis |
| 기원 연도≠ | 2001 | — | 1951 |
| 창시자≠ | Ibrahim, Chen & Sinha | — | Waloddi Weibull |
| 유형≠ | Bayesian time-to-event model | Bayesian linear model | Fully parametric survival regression model |
| 원전≠ | Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer. DOI ↗ | 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. DOI ↗ |
| 별칭≠ | 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 |
| 관련≠ | 4 | 2 | 4 |
| 요약≠ | 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. |
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