A Maximum Likelihood Estimation for Two States Markov-Modulated Poisson Processes
Li Bing · Mohu xitong yu shuxue · 2004
During the last decade, Hidden Markov models(HMMs) have become a widespread tool for modeling sequence of dependent variables. The most important problem in application is how to (estimate) the parameter of the HMMs. By changing continued-time HMMs into discrete-time HMMs is the commom way to deal with it. In this paper, using the similar method we consider maximum (likelihood) estimation for one kind of special HMMs, which is called Markov-modulated Poisson (processes) was given when its state number is 2. Such processes have been proposed for modeling (traffic) streams in complex telecommunication networks.