This graduate textbook provides an alternative to discrete event
simulation. It describes how to formulate discrete event systems, how to
convert them into Markov chains, and how to calculate their transient
and equilibrium probabilities. The most appropriate methods for finding
these probabilities are described in some detail, and templates for
efficient algorithms are provided. These algorithms can be executed on
any laptop, even in cases where the Markov chain has hundreds of
thousands of states. 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.
The material provided should aid practitioners significantly to solve
their problems. This book also provides an interesting approach to
teaching courses of stochastic processes.