Probability and Statistics for Computer Scientists (Hardcover)

Michael Baron

ISBN 10: 1138044482 / ISBN 13: 9781138044487

Published by Taylor & Francis Ltd, London, 2019

About this Item

Hardcover. Praise for the Second Edition:"The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter. [He] has taken great care to select examples that are interesting and practical for computer scientists. . The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and could work well as a required text for an advanced undergraduate or graduate course." ---Computing ReviewsProbability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses.Features:Axiomatic introduction of probabilityExpanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrapNumerous motivating examples and exercises including computer projectsFully annotated R codes in parallel to MATLABApplications in computer science, software engineering, telecommunications, and related areasIn-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). Probability and statistical methods, simulation techniques, and modeling tools. This third edition textbook adds R, including codes for data analysis examples, helps students solve problems, make optimal decisions in select stochastic models, probabilities and forecasts, and evaluate performance of computer systems and networks. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781138044487

Bibliographic Details

• Title: Probability and Statistics for Computer Scientists (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2019
• Language: English
• ISBN 10: 1138044482
• ISBN 13: 9781138044487
• Binding: Hardcover
• Edition: 3rd Edition
• Condition: new

About this title

Synopsis

Praise for the Second Edition:

"The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews

Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses.

Features:

• Axiomatic introduction of probability
• Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap
• Numerous motivating examples and exercises including computer projects
• Fully annotated R codes in parallel to MATLAB
• Applications in computer science, software engineering, telecommunications, and related areas

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).

About the Author

Michael Baron is a professor of statistics at the American University in Washington, DC. 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.