The Bayesian Predictive Distribution in Life Testing Models via MCMC

In reliability studies it is common to not know the population parameters, therefore, it becomes necessary to collect a sample in order to estimate the parameters of the assumed probability distribution. Bayesian methods allow to incorporate subjective information about uncertainties regarding the parameter or parameters of interest. From the bayesian point of view, the uncertainty about the true value of a parameter of interest theta in the population, is modeled by the prior density function pi(theta), (theta is an element of Theta). We will implement the methodology MCMC to obtain the predictive bayesian distributions, which requires the calibration, design, implementation, in addition to the validation of appropriate algorithms.