A Comparison Between Maximum Likelihood Method And Bayesian Method For Estimating Some Non-Homogeneous Poisson Processes Models

Abstract

Abstract

The Non - Homogeneous Poisson  process is considered  as one of the statistical subjects which had an importance in other sciences and a large application in different areas as waiting raws and rectifiable systems method , computer and communication systems and the theory of reliability and many other, also it used in modeling the phenomenon that occurred by unfixed way over time (all events that changed by time).

This research deals with some of the basic concepts that are related to the Non - Homogeneous Poisson process , This research carried out two models of the Non - Homogeneous Poisson process which are the power law model , and Musa –okumto ,   to estimate the parameter of the model that mentioned above , It have been used maximum likelihood method and Bayesian method in the estimation of the parameter that is used in this Research . in order to find the best method in the estimation , we referring to simulation method in which we tested four size of samples ( 25, 50 , 75, 100) to illustrate the effect of changes in samples size on features estimation , Also we suppose four initial value for every parameter from research models parameter and for making a comparison between the used method in estimation as it depend on mean square error (MSE) . As the result referred to that   maximum likelihood method is the best and efficient way in estimation in which it gives the minimum mean square error (MSE).