  Markov processes are popular mathematical models, studied by theoreticians
for their intriguing properties, and applied by practitioners for their
flexible structure. With this book we teach how to model and analyze Markov
processes. We classify Markov processes based on their structural properties,
which in turn determine which analytic methods are required for solving them.
In doing so, we start in each chapter with specific examples that naturally
lead up to general theory and general methods. In this way the reader learns
about Markov processes on the job.
  By studying this book, the reader becomes acquainted with the basic analytic
methods that come into play when systems are modeled as structured Markov
processes. These basic methods will likely prove useful, in real-time when
studying the examples at hand, but more importantly for future encounters with
Markov processes not covered in this book. Methods are more important than
examples. The methods have a large scope of application, even outside the scope
of Markov processes, in areas like probability theory, industrial engineering,
mechanical engineering, physics and financial mathematics.
