This chapter introduces Discrete and Continuous-Time Markov Chain
Process aimed to predict the behavior of a waiting line, based on the probabilities
of going from the state i to the state j, and also from velocity rates lambda and
retracement mu. In both cases a numerical example is provided that shows the mechanics of both random walks as well as the pertinent observations when altering
these parameters, and discusses the possibility of these parameters being altered in
real time in an unsupervised algorithm.
Keywords: Conditional Probabilities, Initial State Vector, Markov Chain Process, Queuing Theory, Steady-State Vector, Transition Matrix
