Bayesian Parameter Estimation and Model Selection for Gallbladder Cancer Data of two Countries

Abstract

The paper proposes statistical model and complete Bayesian inference for cancer survival data of two countries. Complete posterior analysis is done by generating random samples from posterior surface. Gibbs sampler, a Markov chain Monte Carlo (MCMC) method has been used, for generating samples from posterior distribution. The paper also provides algorithm for Gibbs sampler generation scheme for proposed model parameters as well its density estimation. Model compatibility and inter model comparisons, using the measures of Bayesian information criterion (BIC) and deviance information criterion (DIC) has been used. © 2022 NSP