Using Bayesian approach to study robustness of classes of priors for homogeneous and non homogeneous Poison processes

A Non-Homogeneous process is a process with rate parameter (t) such that this rate is a function of time. Bayesians are interested in robustness with respect to changes in prior distributions/sampling models/loss functions. In This work, we focused on replacing a single prior distribution by a class of priors of the parameters of a given Poisson processes, and developing methods of computing the range of the ensuing answers as the prior varied over the class. This approach, called “global robustness”.