Probability and statistics for computer scientists / Michael Baron.

Includes bibliographical references and index.

Includes index.

'Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling ToolsIncorporating 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 EditionAxiomatic 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 bootstrapMore exercises at the end of each chapterAdditional MATLAB{u08EF}des, particularly new commands of the Statistics ToolboxIn-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting 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 SkillsUsing 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'-- Provided by publisher.

'Preface Starting with the fundamentals of probability, this text leads readers to computer simulations and Monte Carlo methods, stochastic processes and Markov chains, queuing theory, statistical inference, and regression. These areas are heavily used in modern computer science, computer engineering, software engineering, and related fields. For whom this book is written The book is primarily intended for junior undergraduate to beginning graduate level students majoring in computer-related fields - computer science, software engineering, information systems, information technology, telecommunications, etc. At the same time, it can be used by electrical engineering, mathematics, statistics, natural science, and other majors for a standard calculus-based introductory statistics course. Standard topics in probability and statistics are covered in Chapters 1-4 and 8-9. Graduate students can use this book to prepare for probability-based courses such as queuing theory, artificial neural networks, computer performance, etc. The book can also be used as a standard reference on probability and statistical methods, simulation, and modeling tools'-- Provided by publisher.