Kishor Shridharbhai Trivedi, PhD, is the Hudson Professor of Electrical and Computer Engineering at Duke University,
Durham, North Carolina. His research interests include computer networks, fault-tolerant computing, modeling tools,
and reliability modeling.
Review
"This introduction...uses Markov chains and other statistical tools to illustrate process in reliability
of computer systems, fault tolerance, and performance."
--SciTech Book News, Vol. 26, No. 2, June 2002
Publisher Web Site, January, 2003
Summary
An accessible introduction to probability, stochastic processes, and statistics for computer science and engineering
applications
This updated and revised edition of the popular classic relates fundamental concepts in probability and statistics
to the computer sciences and engineering. The author uses Markov chains and other statistical tools to illustrate
processes in reliability of computer systems and networks, fault tolerance, and performance.
This edition features an entirely new section on stochastic Petri nets�as well as new sections on system availability
modeling, wireless system modeling, numerical solution techniques for Markov chains, and software reliability modeling,
among other subjects. Extensive revisions take new developments in solution techniques and applications into account
and bring this work totally up to date. It includes more than 200 worked examples and self-study exercises for
each section.
Probability and Statistics with Reliability, Queuing and Computer Science Applications, Second Edition offers a
comprehensive introduction to probability, stochastic processes, and statistics for students of computer science,
electrical and computer engineering, and applied mathematics. Its wealth of practical examples and up-to-date information
makes it an excellent resource for practitioners as well.
Table of Contents
Introduction.
Discrete Random Variables.
Continuous Random Variables.
Expectation.
Conditional Distribution and Expectation.
Stochastic Processes.
Discrete-Time Markov Chains.
Continuous-Time Markov Chains.
Networks of Queues.
Statistical Inference.
Regression and Analysis of Variance.
