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Probability and Statistics for Computer Scientists by Michael Baron: Book with the basics of probability, this text leads readers to computer simulations, statistical inference, and regression. These areas are heavily utilized in modern computer science, computer engineering, software engineering, and related fields.
Probability and Statistics for Computer Scientists is primarily for junior undergraduate to beginner level students majoring in computer science engineering, artificial intelligence, information systems, information technology, telecommunications, etc. At an equivalent time, it can be employed by EE , mathematics, statistics, science , and other majors for a standard calculus-based introductory statistics course.
Standard topics in probability and statistics are in Chapters 1–4 and 8–9. Graduate students can use this book to organize for probability-based courses like queuing theory, artificial neural networks, computer performance, etc. The book also can be used as a typical reference on probability and statistical methods, simulation, and modeling tools.
The text is especially recommended for a one-semester course with several open-end options available. Probability-oriented course. Proceed to Chapters 6–7 for Stochastic Processes, Markov Chains, and Queuing Theory.
Author of “Probability and Statistics for Computer Scientists“ is Michael Baron
Probability and Statistics for Computer Scientists by Michael Baron
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.
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 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).
Table of Content
Introduction and Overview
Probability and Random Variables
Probability and Statistics for Computer Scientists PDF
Author(s): Michael Baron
Publisher: Chapman and Hall/CRC