Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Usajili wa Bayesian× | Regressioni ya Kuishi ya Weibull ya Parametric× | |
|---|---|---|
| Nyanja≠ | Mbinu za Bayes | Uchanganuzi wa Uhai |
| Familia≠ | Bayesian methods | Survival analysis |
| Mwaka wa asili≠ | — | 1951 |
| Mwanzilishi≠ | — | Waloddi Weibull |
| Aina≠ | Bayesian linear model | Fully parametric survival regression model |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| Zinazohusiana≠ | 2 | 4 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
|
|