Probability and Statistics for Computer Scientists, 2nd Edition

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses. Written in a lively style with simple language, this classroom-tested book can now be used in both one- and two-semester courses.

New to the Second Edition

• Axiomatic introduction of probability
• Expanded coverage of statistical inference, including standard errors of estimates and their estimation, inference about variances, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap
• More exercises at the end of each chapter
• Additional MATLAB^® codes, particularly new commands of the Statistics Toolbox

In-Depth yet Accessible Treatment of Computer Science-Related Topics
Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET).

Encourages Practical Implementation of Skills
Using simple MATLAB commands (easily translatable to other computer languages), the book provides short programs for implementing the methods of probability and statistics as well as for visualizing randomness, the behavior of random variables and stochastic processes, convergence results, and Monte Carlo simulations. Preliminary knowledge of MATLAB is not required. Along with numerous computer science applications and worked examples, the text presents interesting facts and paradoxical statements. Each chapter concludes with a short summary and many exercises.

Table of Contents

Chapter 1: Introduction and Overview

Part I: Probability and Random Variables
Chapter 2: Probability
Chapter 3: Discrete Random Variables and Their Distributions
Chapter 4: Continuous Distributions
Chapter 5: Computer Simulations and Monte Carlo Methods

Part II: Stochastic Processes
Chapter 6: Stochastic Processes
Chapter 7: Queuing Systems

Part III: Statistics
Chapter 8: Introduction to Statistics
Chapter 9: Statistical Inference I
Chapter 10: Statistical Inference II
Chapter 11: Regression

Part IV: Appendix
Chapter 12: Appendix