Bayesian Estimation of Tabular Survival Models from Complete Samples

The problem of estimation of a tabular survival model from complete samples is considered from a Bayesian approach. If a life test involves a large cohort group, then data is typically given as a grouped data set. The survival function S(t) is usually estimated the moment estimator. We approximate the joint prior distribution of the unknown unconditional failure probabilities by a Dirichlet distribution, and then use a linear relationship between S (t) and the conditional survival probabilities to obtain a Bayes estimator of S (t).