  In the study of life tables the random variable of interest is usually
assumed discrete since mortality rates are studied for integer ages. In dynamic
life tables a time domain is included to account for the evolution effect of
the hazard rates in time. In this article we follow a survival analysis
approach and use a nonparametric description of the hazard rates. We construct
a discrete time stochastic processes that reflects dependence across age as
well as in time. This process is used as a bayesian nonparametric prior
distribution for the hazard rates for the study of evolutionary life tables.
Prior properties of the process are studied and posterior distributions are
derived. We present a simulation study, with the inclusion of right censored
observations, as well as a real data analysis to show the performance of our
model.
