Probability and Statistics for Computer Scientists - Rilegato

Baron, Michael

Rilegato

ISBN 10: 1439875901 ISBN 13: 9781439875902

Casa editrice: Chapman and Hall/CRC, 2013

Riassunto

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 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 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.

Su questo libro

Informazioni sull?autore

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.

Dettagli dell'edizione

• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 1439875901
• ISBN 13: 9781439875902
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 450
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile