পদ্ধতির তুলনা করুন

	বেয়েশীয় রিগ্রেশন	Cox Proportional Hazards Regression	Kaplan-Meier Survival Estimator	Weibull Parametric Survival Regression
ক্ষেত্র	বেইসীয়	উত্তরজীবিতা	উত্তরজীবিতা	উত্তরজীবিতা
পরিবার	Bayesian methods	Survival analysis	Survival analysis	Survival analysis
উদ্ভবের বছর	—	1972	1958	1951
প্রবর্তক	—	Cox, D. R.	Kaplan, E. L. & Meier, P.	Waloddi Weibull
ধরন	Bayesian linear model	Semi-parametric hazard regression model	Non-parametric survival estimator	Fully parametric survival regression model
মৌলিক উৎস	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	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
অপর নাম	bayesian linear regression, probabilistic regression, bayesian regresyon	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	product-limit estimator, km curve, kaplan-meier sağkalım analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
সম্পর্কিত	2	3	2	4
সারসংক্ষেপ	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.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.	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.