Synopses & Reviews
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.
Synopsis
This book relates fundamental concepts in probability and statistics to the computer sciences. The author uses Markov chains and other statistical tools to illustrate processes such as reliability of computer systems and fault tolerance. This new, revised edition includes a new chapter on Stochastic Petri Nets.
About the Author
KISHOR S. 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.
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.
Appendix A: Bibliography.
Appendix B: Properties of Distributions.
Appendix C: Statistical Tables.
Appendix D: Laplace Transforms.
Appendix E: Program Performance Analysis.