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ANNOTATED BIBLIOGRAPHY
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Chapter 1 and Sections 2.1 to 2.4 introduce Petri nets. Sections 3.1 to 3.3 introduce timed Petri nets. Chapters 5 and 6 introduce GSPNs and their properties and
analysis.
[7] M. K. Molloy. Fundamentals of Performance Modeling. Macmillan Publishing Co., New
York, 1989.
Chapters 1 and 2 introduce probability theory, and Chapter 5 introduce Markov
chains.
[8] R. Nelson. Probability, Stochastic Processes, and Queueing Theory: The Mathematics of
Computer Performance Modeling. Springer-Verlag, New York, 1995.
This is a well-written book on probability and Markov chains containing a lot of
examples. Chapters 1, 2, and 4 and Section 5.1 introduce probability theory, Sections 6.1 and 6.2 cover Poisson processes, and Sections 8.1 to 8.4 discuss Markov
chains and their properties.
[9] S. M. Ross. Introduction to Probability Models. Academic Press, Amsterdam, 8th edition,
2003.
[10] W. J. Stewart. Introduction to the Numerical Solution of Markov Chains. Princeton University
Press, Princeton, N.J., 1994.
This is a classic, widely cited book on the subject. It gives in-depth understanding
of how some of the solvers in Möbius work.
[11] K. S. Trivedi. Probability and Statistics with Reliability, Queuing, and Computer Science
Applications. John Wiley & Sons, New York, 2nd edition, 2002.
This is a strongly recommended, comprehensive, and well-written book that covers a wide variety of material required for sound understanding of the mathematical
concepts used in modeling stochastic systems. The following chapters are particularly useful: Chapter 1, Sections 2.1 to 2.4, 2.9, 3.1 to 3.5, 3.8, 3.9, 4.1, 4.2, 4.6,
6.1 to 6.4, and Chapter 8