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Probability and Statistics for Computer Scientists, Second Edition / Edition 2

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Overview

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.

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Editorial Reviews

From the Publisher
Praise for the Second Edition:
"The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter in statistics. Since the author has taken great care to select examples that are interesting and practical for computer scientists, the book should hold special appeal for that group. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and demands a reader with a firm background in calculus. It could work well as a required text for an advanced undergraduate or graduate course."
—James Van Speybroeck, Computing Reviews, January 2014

Praise for the First Edition:
"… students of all majors will benefit from the author’s fine presentation of applied probability models and computer simulation. I am seriously considering adopting it for a [probability-oriented course] … the chapters on simulation and applied probability models are truly outstanding …"
—Matthew A. Carlton, The American Statistician, August 2008

“…well-organized text seems designed as a gentle introduction to the mathematics of probability and statistics. …helpful diagrams…surprisingly detailed.”
—John Maindonald, International Statistical Review, Vol. 75, No. 2, 2007

"… an ideal textbook for computer science students. … This book is primarily intended for junior undergraduate to beginning graduate level students majoring in computer-related fields. It can also be used by electrical engineering, mathematics, statistics, actuarial science, and other majors for a standard introductory statistics course. Graduate students can use this book to prepare for probability-based courses such as queuing theory, artificial neural networks, and computer performance. Overall, this well-written text can be used as a standard reference on probability and statistical methods, simulation and modeling tools."
Journal of the Royal Statistical Society

"The book represents a good reference to all who are interested in statistics, modeling stochastic processes, and computer simulation. … The book’s material is invaluable and presented with clarity …"
Journal of Applied Statistics, 2007

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Product Details

  • ISBN-13: 9781439875902
  • Publisher: Taylor & Francis
  • Publication date: 8/14/2013
  • Edition description: Revised
  • Edition number: 2
  • Pages: 473
  • Sales rank: 1,021,330
  • Product dimensions: 7.00 (w) x 10.00 (h) x 1.10 (d)

Meet the Author

Michael Baron is a professor of statistics at the University of Texas at Dallas. He has published two books and numerous research articles and book chapters. Dr. Baron is a fellow of the American Statistical Association, a member of the International Society for Bayesian Analysis, and an associate editor of the Journal of Sequential Analysis. In 2007, he was awarded the Abraham Wald Prize in Sequential Analysis. His research focuses on the use of sequential analysis, change-point detection, and Bayesian inference in epidemiology, clinical trials, cyber security, energy, finance, and semiconductor manufacturing. He received a Ph.D. in statistics from the University of Maryland.

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Table of Contents

Introduction and Overview
Making decisions under uncertainty
Overview of this book

Probability and Random Variables
Probability
Sample space, events, and probability
Rules of Probability
Equally likely outcomes. Combinatorics
Conditional probability. Independence

Discrete Random Variables and Their Distributions
Distribution of a random variable
Distribution of a random vector
Expectation and variance
Families of discrete distributions

Continuous Distributions
Probability density
Families of continuous distributions
Central limit theorem

Computer Simulations and Monte Carlo Methods
Introduction
Simulation of random variables
Solving problems by Monte Carlo methods

Stochastic Processes
Stochastic Processes
Definitions and classifications
Markov processes and Markov chains
Counting processes
Simulation of stochastic processes

Queuing Systems
Main components of a queuing system
The Little’s Law
Bernoulli single-server queuing process
M/M/1 system
Multiserver queuing systems
Simulation of queuing systems

Statistics
Introduction to Statistics
Population and sample, parameters and statistics
Simple descriptive statistics
Graphical statistics

Statistical Inference I
Parameter estimation
Confidence intervals
Unknown standard deviation
Hypothesis testing
Inference about variances

Statistical Inference II
Chi-square tests
Nonparametric statistics
Bootstrap
Bayesian inference

Regression
Least squares estimation
Analysis of variance, prediction, and further inference
Multivariate regression
Model building

Appendix
Appendix

Inventory of distributions
Distribution tables
Calculus review
Matrices and linear systems
Answers to selected exercises

Index

Summary, Conclusions, and Exercises are included at the end of each chapter.

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