Abstract: "In this paper, we study the total reward connected with a Markov reward process from time zero to time m.

We believe that this is the first paper which uses Semi-Regenerative Theory for developing numerical (nonsimulation) algorithms to find the steady-state distribution of a Markov chain.

This book features the probabilistic interpretation of Gaussian elimination, a concept that unifies many of the topics covered, such as embedded Markov chains and matrix analytic methods.