The teaching of appliedprobability needs a fresh approach. The fieldof applied probability has changedprofound ly in the past twenty years andyet the textbooks in use today do not fully reflect the changes. The development of computational methods has greatly contributed to a better understanding of the theory. It is my conviction that theory is better understood when the algorithms that solve the problems the theory addresses are presented at the same time. This textbook tries to recognize what the computer can do without letting the theory be dominated by the computational tools. In some ways, the book is a successor of my earlier book Stochastic Modeling and Analysis. However, the set-up of the present text is completely different. The theory has a more central place and provides a framework in which the applications fit. Without a solidbasis in theory, no applications can be solved. The book is intended as a first introduction to stochastic models for senior undergraduate students in computer science, engineering, statistics and operations research, among others. Readers of this book are assumed to be familiar with the elementary theory of probability.
I am grateful to my academic colleagues Richard Boucherie, Avi Mandelbaum, Rein Nobel andRien van Veldhuizen for their helpful comments, andto my students Gaya Branderhorst, Ton Dieker, Borus Jungbacker and Sanne Zwart for their detailed checking of substantial sections of the manuscript. Julian Rampelmann andGloria Wirz-Wagenaar were helpful in transcribing my handwritten notes into a nice Latex manuscript.
Finally, users of the book can findsupporting educational software for Markov chains andqueues on my website http://staff.feweb.vu.nl/tijms.
