Open Markov Type Population Models: From Discrete to Continuous Time

We address the problem of finding a natural continuous time Markov type process—in open populations—that best captures information provided by an chain in discrete which is usually sole possible observation from data. Given chain, we single out two main approaches: In first one, consider calibration procedure process using transition matrix and show that, when embeddable has optimal solutions. second approach, semi-Markov processes—and schemes—and propose direct extension theory to one known structure representation result for processes that decomposes as sum terms given products random variables functions built adequate increasing sequence stopping times.