Page 9 - A First Course In Stochastic Models

Preface

The teaching of applied probability needs a fresh approach. The field of applied
probability has changed profoundly in the past twenty years and yet 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 solid basis 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 and Rien van Veldhuizen for their helpful comments, and to my stu-
dents Gaya Branderhorst, Ton Dieker, Borus Jungbacker and Sanne Zwart for their
detailed checking of substantial sections of the manuscript. Julian Rampelmann
and Gloria Wirz-Wagenaar were helpful in transcribing my handwritten notes into
a nice Latex manuscript.
Finally, users of the book can find supporting educational software for Markov
chains and queues on my website http://staff.feweb.vu.nl/tijms.